Mlops jobs

...(OpenAI, Anthropic, Cohere, open-source) and outline the data, prompt-engineering, RAG, and governance strategy required for safe deployment in regulated BFSI environments. • Produce at least two working PoCs that demonstrate measurable gains in first-contact resolution, average handling time, or cost-to-serve, ready for pilot rollout with a flagship NBFC. • Define a phased talent, tooling, and MLOps plan so our product and delivery teams can scale these capabilities across the rest of the platform. • Establish success metrics and a commercial model that converts automation gains into new subscription or usage-based revenue streams. Success will be measured by a signed-off roadmap, live PoCs, and an implementation blueprint that product, engineering, and sal...

...backend services, APIs, dashboards, and customer-facing features - Support ML/MLOps workflows including model training and deployment - Implement monitoring, error handling, and automated recovery systems - Ensure data quality, validation, and anomaly detection - Design reusable, scalable data schemas and APIs - Contribute to CI/CD pipelines with automated testing and deployments Required Skills: - Strong Python (5+ years), SQL, Pandas/PySpark - AWS expertise (Lambda, Glue, S3, Athena, DynamoDB, ECS Fargate) - Experience with data lakes and serverless architectures - Backend/API development (REST, microservices, event-driven systems) - Working knowledge of Node.js or willingness to learn - Exposure to ML/MLOps (SageMaker, model lifecycle) - Experience with large-scale da...

This training program is designed to provide participants with practical knowledge of building, deploying, and managing Machine Learning solutions usi...knowledge of building, deploying, and managing Machine Learning solutions using Google Cloud Platform (GCP). The program focuses on real-time industry use cases, hands-on implementation, and cloud-based ML workflows. Participants will gain experience in: Data preparation and preprocessing on GCP Building and training ML models Using Vertex AI and other GCP ML services Model deployment and monitoring MLOps concepts and automation Real-world project implementation using cloud-based ML solutions The training aims to help professionals apply GCP-ML concepts in enterprise projects and prepare for real-time business scenarios and certi...

...platform features - Contribute to frontend dashboards (Vue.js / ) - Support ML/MLOps workflows (training, deployment, lifecycle on SageMaker) - Implement monitoring, error handling & ensure high system reliability (99.9% uptime) - Build data validation, quality checks & anomaly detection systems - Design systems for backfills, reprocessing & consistency - Maintain data contracts, schema versioning & CI/CD pipelines --- Required Skills - 3–7+ years of software/data engineering experience - Strong Python (5+ yrs), SQL, Pandas/PySpark - Hands-on AWS (Lambda, Glue, S3, Athena, DynamoDB, ECS, Step Functions) - Experience with REST APIs, microservices, event-driven architecture - Knowledge of ML/MLOps (SageMaker, model lifecycle) - Exposure to Node.js (...

This is a focused 1-day task. The LLM pipeline already exists in Python — I need it containerized with Docker and running live on AWS by end of day. No research, no exploration — come ready to execute. Deliverables: 1. Dockerfile & Docker Compose Write a production-grade Dockerfile for the LLM inference pipeline. Multi-stage if needed, minimal image size, env vars for config and secrets. 2. AWS deployment Deploy the container to AWS (ECS Fargate preferred, or EC2 if GPU required). Expose a working endpoint. Basic IAM role and security group setup. 3. Smoke test + handoff Confirm the endpoint is live and responding. Provide a short Bash script or README so I can redeploy independently. No undocumented magic. YOU'RE A FIT IF: ✔️ You've deployed a containerized ...

...my raw data, handle preprocessing, select and justify the algorithm, train, tune, and deliver a production-ready model with accompanying Python code (TensorFlow, PyTorch, or scikit-learn are all acceptable). Your proposal should focus on your relevant experience with similar supervised learning projects—please highlight datasets you have tackled, algorithms you excel with, and any deployment or MLOps know-how. No lengthy sales pitches; I want to see evidence that you can translate business objectives into high-performing, explainable models. Deliverables • Clean, well-commented source code and notebooks • Reproducible training pipeline • Evaluation report covering accuracy and at least one additional metric appropriate to the problem (e.g., F1, ROC-AUC)...

Urgent Hire | Python RPA Engineer — NLP + Computer Vision What We're Building We're working on a time-sensitive automation project that requires a battle-tested Python RPA developer who can hit the ground running — no handholding, no ramp-up...Selenium, you must have shipped real projects with these • Computer Vision — TensorFlow, offline models only (zero external API calls) • NLP — spaCy-based pipelines, offline and self-contained • Neural Network design — hands-on architecture and training experience, not just fine-tuning wrappers • Web scraping at scale — robust, fault-tolerant implementations Good to Have • MLOps experience — model versioning, deployment, monitoring • Exp...

I am look...experience with PyTorch or TensorFlow Proven experience in computer vision (CNNs, image classification) Experience with active learning / human-in-the-loop systems Understanding of model calibration and uncertainty estimation Ability to design production-ready ML pipelines Nice to Have: Experience with medical or biological image data Familiarity with annotation tools (e.g., Label Studio or similar) MLOps / deployment experience Please share: Examples of similar projects (especially active learning or HITL systems) Your approach to implementing uncertainty-based sampling Suggested improvements you would explore for our use case We are looking for someone who can think critically about the system and help us significantly reduce manual effort while improving model pe...

...27001 / GDPR readiness) through secure-by-default engineering practices CORE SKILLS REQUIRED BACKEND Node.js, Python (FastAPI/Flask), RESTful APIs, microservices, PostgreSQL, Redis, message queues FRONTEND React, TypeScript, responsive UI, dashboard systems, data visualisation (D3 / Recharts) MACHINE LEARNING ML model deployment, fraud detection pipelines, risk scoring, scikit-learn / PyTorch, MLOps basics CLOUD & DEVOPS AWS (Lambda, RDS, S3, API Gateway), Docker, CI/CD (GitHub Actions), infrastructure as code REGTECH / COMPLIANCE AML, KYC/KYB, PEP/Sanctions, identity document verification, GDPR, FCA regulatory context SECURITY Secure API design, OAuth2/JWT, data encryption, OWASP best practices, role-based access control EXPERIENCE & QUALIFICATIONS 5+ years of profession...

I’m scheduling an advanced, hands-on course that will run from 27 April 2026 for one to two weeks. The participants—data scientists, data engineers, DevOps and MLOps engineers—already work daily with Spark and cloud pipelines; what they need is a deep dive into building production-grade Databricks AI agents. Beyond code walkthroughs, I expect you to cover the entire lifecycle: designing agentic AI workflows, wiring those agents to Databricks clusters and MCP servers, operationalising them with MLflow and Unity Catalog, and enforcing governance at scale. The emphasis throughout the program should stay on Databricks AI agents, as that is the area the team must master. To shortlist you, I’ll need the following: • Your portfolio that proves you have del...

...advanced, but I want to reinforce the foundations and then push further into Machine Learning, Deep Learning, and Data Visualization while working through real business problems. Scope • Daily live sessions (about 60–120 minutes, Monday-Friday) for 12 consecutive weeks. • A structured curriculum that begins with a quick Python + statistics refresher and moves swiftly into sophisticated modelling, MLOps, and the latest AI techniques. • Practical, code-along labs in Jupyter or VS Code after every concept—no passive slide decks. • One continuous real-time financial-modelling project (e.g., credit-risk scoring, portfolio optimisation, or time-series forecasting) plus smaller mini-assignments. • A capstone that demonstrates the full pipe...

We are looking for a skilled AI Engineer to...experience as an AI Engineer, Machine Learning Engineer, or similar role Strong knowledge of Python and libraries such as TensorFlow, PyTorch, or scikit-learn Experience with APIs and model deployment (Docker, Kubernetes, or cloud platforms like AWS/Azure/GCP) Familiarity with LLMs, NLP, or computer vision is a plus Ability to communicate clearly and work independently Nice to Have: Experience with MLOps pipelines Background in data engineering or DevOps Experience integrating AI into production systems Project Details: Type: Freelance / Contract Duration: To be discussed Budget: Open (based on experience) Start: ASAP If you’re interested, please share: Your portfolio or previous AI projects Relevant experience Availability a...

...also comfortable branching into Reinforcement Learning or NLP later, that flexibility will be a plus for the longer roadmap, but the immediate priority is Deep Learning mastery. The sessions will be delivered live (online or hybrid can be arranged), and I’ll rely on you to: • Shape a clear, week-by-week syllabus covering CNNs, RNNs, transformers, optimisation tricks, model interpretability and MLOps basics using Python, TensorFlow or PyTorch • Provide concise slide decks, hands-on notebooks (Jupyter/Colab) and at least three graded mini-projects that mirror industry use-cases • Guide learners through code reviews and Q&A, then wrap up with a capstone evaluation and feedback report All teaching material must be original or properly licensed, and rea...

...single requirement, not multiple specialist roles. The person should be strong across modern AI engineering and capable of taking problems from architecture and prototyping through optimization, deployment, and production readiness. The work may span LLMs / SLMs, recommendation engines, agentic interview workflows, AI-based result assessments, multimodal AI systems, classical ML, deep learning, and MLOps. This role is best suited for someone who is a strong AI generalist with solid engineering discipline and the ability to convert ambiguous problem statements into practical, scalable AI systems. The source role requires 5+ years of experience entirely in the AI/ML domain. What You Will Work On - Design and build AI solutions across multiple use cases and workstreams Develop a...

My website offers AI consulting plus machine-learning and data services, and I want to strengthen its search visibility through strategic backlink outreach only. I’m focused on earning do-follow links from reputable technology blogs with solid domain authority and a genuine readership interested in artificial intelligence, data science, MLOps, cloud, and related topics. What I need from you: • Research and compile a list of high-quality tech blogs that accept external contributions or link placements. • Pitch and secure contextual backlinks that read naturally inside relevant, value-adding articles or resource pages. • Provide a brief placement report for each live link, including URL, anchor text, DA/DR, and publication date. I don’t currently have a ta...

...is an initial qualifier to assess fit before we share further details. Please respond only if you are based in Jaipur, Rajasthan and can support in-person collaboration if needed. --- We'd like you to share: 1. Brief company/individual profile – who you are and how long you've been active in AI/ML 2. Core areas of expertise – e.g., NLP, computer vision, predictive modeling, data pipelines, MLOps, LLMs, etc. 3. 2–3 relevant past projects – industry, problem solved, tools/tech used (no confidential details needed) 4. Team size & structure – solo practitioner, small team, or company? 5. Availability – current bandwidth and ability to take on a new engagement 6. Hourly charges – please quote in INR (₹/hour). If you prefer a ...

...with the core orchestrator, no third part tool. • Web navigation/ scraping with Selenium/Playwright: document download, classification, OCR/text extraction. • Build/train neural networks (e.g., CNNs for image doc classification). • NLP expertise with spaCy for entity extraction. • Computer vision using TensorFlow/OpenCV (offline Vision Libraries preferred). Preferred Skills: • MLOps (e.g., MLflow, Docker for deployment). • Strong problem-solving for complex, error-prone workflows. • 2+ years portfolio with RPA/CV projects (GitHub links required). Project Details: • Milestones: Week 4 (scraper prototype), Week 8 (CV model), Week 12 (full RPA pipeline). • Tools: Python 3.10+, Git, Jupyter. Patient, met...

...systems (TTS, STT, STS) Experience in LLM fine-tuning, quantization, and model optimization Ability to deploy self-hosted / offline AI models Strong backend development skills (Python, Node.js, FastAPI, Django, etc.) Experience designing scalable, modular backend architectures Familiarity with vector databases (FAISS, Milvus, Pinecone, Weaviate, etc.) Understanding of cloud, containers, and MLOps (Docker, Kubernetes is a plus)...

I need an experienced AI/ML freelancer to take the lead on building a custom copilot that streamlines day-to-day clinical and administrative workflows for a healthcare platform. The immediate goal is to move from scattered manual steps to an intelligent...Slack/Teams bot • At least three predefined healthcare workflows automated end-to-end (e.g., visit note drafting, prior-auth request, lab follow-up) • Clear README with setup, environment variables, and model/embedding choices • Security checklist confirming HIPAA-ready data handling and access controls Once the copilot’s core loop is stable, we can expand into fine-tuning, evaluation, and full MLOps deployment, but the focus right now is that initial working assistant. I’m ready to start as soon a...

...enforcing context bound generation and preventing hallucination outside retrieved evidence. Indexing is parallelized using ProcessPoolExecutor for efficient multi core utilization and automatically scales to distributed ingestion via PySpark when corpus size exceeds a configured threshold, enabling safe handling of 20k plus documents or 50GB class corpora, while the system is wrapped in a full MLOps backbone that integrates MLflow for experiment tracking of retrieval metrics, PPO reinforcement learning rewards, and parameter tuning, exposes Prometheus metrics for latency and retrieval monitoring compatible with Grafana dashboards, and supports Airflow DAG orchestration for scheduled indexing and policy training workflows. Reinforcement learning is implemented using a PyTorch base...

Azure MLOps Trainer Needed for Training Sessions We are hiring a senior Azure AI engineer to provide structured, hands-on training in building enterprise-grade LLM systems. This is NOT a beginner AI course and NOT a chatbot project. We need practical training in implementing: - Azure OpenAI API integrations (production-ready) - Full RAG pipelines using Azure AI Search (vector + hybrid search) - Document ingestion workflows (Blob > OCR > chunking > embeddings) - Function/tool-calling for agentic workflows - Secure deployment using Azure Functions / Container Apps / AKS - Logging, retries, structured validation, and reliability patterns - Enterprise constraints (RBAC, private endpoints, managed identity) The focus is: - Clean architecture - Production patterns - Observabi...

Senior AI / ML Architect – GenAI, MLOps & Enterprise AI Work Support (10+ Years) Job Description We are seeking a highly experienced AI/ML professional (10+ years) to provide ongoing technical work support across advanced AI, GenAI, and data-driven systems. This role involves hands-on guidance, design reviews, problem-solving, and production support for complex AI/ML implementations in enterprise environments. The ideal candidate has deep real-world experience and can quickly understand requirements, identify gaps, and provide clear technical direction. Candidates may specialize in any subset of the skills listed below. Core Expertise (Any of the Below) Generative AI & LLM Systems LLM-based applications and enterprise GenAI platforms Prompt design, alignment, ...

...automatically scouts freelancing websites, general job boards, and specialised training platforms for roles or courses that involve artificial-intelligence work. The agent must: • Crawl and scrape the relevant pages in real time or on a frequent schedule. • Apply NLP or other classification techniques to decide whether a posting is truly AI-related, then tag it by sub-domain (e.g. vision, NLP, MLOps, prompt-engineering). • Deliver concise, deduplicated listings to me through an in-app notification feed—no email or SMS required. For the deployment side I’m open to Python (Scrapy, BeautifulSoup, Selenium), Node, or any stack you are comfortable with so long as it is containerised and can run unattended on a small cloud instance. A lightweight web in...

...criteria A working API must return real-time predictions within agreed latency limits, integrate seamlessly with the current SMTP/ESP workflow, and include logging for compliance review. Final delivery is considered complete when the system runs in production and all documentation passes peer review. Tools & stack Python, scikit-learn or TensorFlow, SQL/NoSQL for data storage, and standard MLOps utilities (Docker, CI/CD) are anticipated, yet alternative libraries are welcome if they meet the same reliability and security standards. Timeline and milestones will be outlined together at project start, with code reviews scheduled at each major checkpoint....

...model is validated I’ll ask you to craft intuitive dashboards that highlight drivers, confidence ranges and any red-flag anomalies the model detects. Solid statistical grounding is essential; I want clear explanations of feature importance, assumptions and limitations that business stakeholders can grasp quickly. Big-data exposure, cloud familiarity (Azure, AWS or GCP), ETL pipeline design and MLOps practices are all welcome extras—you’ll have room to propose improvements if they make the solution more robust or scalable. Deliverables I need from you: • A well-documented predictive model with reproducible code and clear version control • Cleaned and transformed datasets stored back into SQL (or a recommended alternative) • An interactive Pow...

...Ensure data quality, security, and model performance optimization Required Skills & Qualifications: • 10+ years of experience in AI/ML or Software Engineering roles • Strong proficiency in Python and data processing libraries (NumPy, Pandas) • Hands-on experience with TensorFlow, PyTorch, Scikit-learn • Strong understanding of Deep Learning, NLP, Computer Vision • Experience with Model Deployment & MLOps pipelines • Experience working with Cloud platforms (AWS / Azure / GCP) • Strong knowledge of Data Engineering & Big Data tools • Experience with REST APIs and Microservices • Excellent analytical and communication skills • Experience with Generative AI and LLM frameworks • Knowledge of Docker, Kubernetes • ...

...Required Qualifications 8+ years of experience as an AI Architect or similar role, with proven expertise in AI/ML technologies (e.g., TensorFlow, PyTorch, GPT models, RAG systems). Strong background in bridging business needs to technical implementations, demonstrated through successful projects with short development cycles. Proficiency in cloud platforms (AWS, Azure, GCP) for AI deployment, MLOps, and scalable architectures. Experience in data engineering, NLP, computer vision, or predictive modeling relevant to business applications. Excellent communication skills to articulate complex AI concepts to non-technical stakeholders. Bachelor's or Master's degree in Computer Science, AI, or a related field. Preferred Qualifications Prior experience in travel, hospitalit...

...systems (TTS, STT, STS) Experience in LLM fine-tuning, quantization, and model optimization Ability to deploy self-hosted / offline AI models Strong backend development skills (Python, Node.js, FastAPI, Django, etc.) Experience designing scalable, modular backend architectures Familiarity with vector databases (FAISS, Milvus, Pinecone, Weaviate, etc.) Understanding of cloud, containers, and MLOps (Docker, Kubernetes is a plus)...

...best practices. Required Skills Strong understanding of AI/ML concepts such as: Predictive analytics, forecasting, classification, NLP/LLM, GenAI, RPA, model evaluation, etc. Ability to translate business problems into AI/ML use cases. Ability to communicate complex technical concepts to non-technical audiences. Familiarity with modern data platforms, cloud providers (AWS/Azure/GCP), and MLOps practices. Experience in proposal creation, solution pitching, and pre-sales cycles. Acceptance Criteria Minimum 5 years of experience in AI/ML solutioning, consulting, or presales. Experience leading client discussions and presenting solutions. Demonstrated ability to define business use cases and value propositions. Exposure to enterprise customers or large-scale business tran...

...while ensuring compliance, deliverability, and scalability. We are deliberately not prescribing tools. You are expected to choose the right architecture and tooling based on the requirements below. What We Need Built (Outcomes) 1. Demand Signal Detection System: Design a system that can automatically identify companies that are actively hiring for roles such as LLM Trainers, AI / ML Engineers, MLOps Engineers, Model Evaluators, AI Researchers. The system should prioritize high-intent signals, such as recent job postings, repeated hiring for similar roles, active recruiter or TA hiring activity, output should be a daily, refreshed list of qualified companies 2. Account & Contact Mapping: For each qualified company, the system should: - Identify the right decision-layer co...

...suggested timings. Packaged folder: PPTX/Google Slides, PDF handouts, images, quiz spreadsheet, rubric files. Courses to produce (high level) Data Literacy & Governance (Dummy→Hero, templates for audit, DPIA, stewardship) AI Literacy, Governance & Security (model audit, privacy for ML, incident playbooks) AI Mastery — Advanced (deep dives: evaluation, interpretability, advanced privacy, production MLOps best practices, research-to-production workflows) AI Graphics & Video Editing (practical: prompt engineering for visuals/video, workflows for AI-assisted editing, tools pipeline, export-ready assets) Required skills & experience (must have) Instructional design for adult learners (tech/enterprise audiences). Strong slide-deck design skills &mdas...

...with? How do you typically define and defend novelty and contribution in applied research? Describe your experience responding to reviewer comments. Give a sample of Similar Books you have published and publishers you worked with ======================================= The works are based on original material (slides, transcripts, frameworks) focused on AI infrastructure, data centers, energy, MLOps, and applied AI economics. additional projects in clude -AI Literacy, Governance & Security; Data Literacy & Governance This is not marketing content or SEO writing. We are looking for someone who understands peer-reviewed publishing, scholarly contribution, and editorial positioning. Scope of Work Journal Paper Shape a journal-ready manuscript aligned to Q1/Q2 journals ...

...devoted to pushing the limits of artificial intelligence, and I’m ready to bring a committed AI software engineer into the core team. You will own the software layer of our AI stack—designing training pipelines, shaping inference services, integrating state-of-the-art models, and turning ambitious ideas into production-ready code. Expect to work hands-on with Python, PyTorch or TensorFlow, modern MLOps tooling (Docker, Kubernetes, CI/CD), and a cloud platform such as AWS or GCP. Because we’re still small, your voice will matter. Whether your strongest suit is classic machine learning, NLP, computer vision, or another specialty, it’s the ability to turn theory into robust, well-tested code that counts. Deliverables • An initial proof-of-concept ser...

...passionate about solving real-world problems using AI and security-driven approaches. I enjoy working at the intersection of machine learning, cybersecurity, and data science, with a particular interest in secure machine learning systems, threat detection, and intelligent data modeling. Key Skills: Machine Learning, Cybersecurity, Cryptography, Data Analysis, Statistical Modeling, Python, Java, C, SQL, MLops (Airflow, Mlflow, Docker, FastAPI), Forensics Tools, Network Security (Wireshark), SPSS, SAS, R....

...Landing) using VideoMAE or TimeSformer. Data Correlation: Translate biomechanical outputs into performance scores and predictive potential markers. Required Technical Stack Languages/Frameworks: Python, PyTorch (Advanced). Vision: YOLO v8-v11, RT-DETR, Ultralytics, DeepLabCut, SLEAP. Spatio-Temporal: VideoMAE, Video Swin Transformer, TimeSformer. Deployment/Optimization: ONNX Runtime, TensorRT, MLOps (Weights & Biases / MLflow). Mandatory Candidate Qualifications We are looking for a top-tier expert. Please only apply if you meet the following criteria: Serious References: You must provide verifiable case studies or GitHub repositories of similar complex computer vision projects (pose estimation, motion analysis, or animal tracking). Experience: At least 5+ years of...

...Landing) using VideoMAE or TimeSformer. Data Correlation: Translate biomechanical outputs into performance scores and predictive potential markers. Required Technical Stack Languages/Frameworks: Python, PyTorch (Advanced). Vision: YOLO v8-v11, RT-DETR, Ultralytics, DeepLabCut, SLEAP. Spatio-Temporal: VideoMAE, Video Swin Transformer, TimeSformer. Deployment/Optimization: ONNX Runtime, TensorRT, MLOps (Weights & Biases / MLflow). Mandatory Candidate Qualifications We are looking for a top-tier expert. Please only apply if you meet the following criteria: Serious References: You must provide verifiable case studies or GitHub repositories of similar complex computer vision projects (pose estimation, motion analysis, or animal tracking). Experience: At least 5+ years of...

Key Responsibilities MLOps Responsibilities: Collaborate with data scientists to operationalize ML workflows. Build complete ML pipelines with Airflow, Kubeflow Pipelines, or Metaflow. Deploy models using KServe, Seldon Core, BentoML, TorchServe, or TF Serving. Package models into Docker containers using Flask or FastAPI or Django for APIs. Automated dataset versioning & model tracking via DVC and MLflow. Setup model registries and ensure reproducibility and audit trails. Implement model monitoring for: (i) Data drift and schema validation (using tools like Evidently AI, Alibi Detect). (ii) Performance metrics (accuracy, precision, recall). (iii) Infrastructure metrics (latency, throughput, memory usage). Implement event-driven retraining workflows triggered by drift alerts ...

...agency—you’ll shape our voice and distribution based on the initial content we share. Direct access to founders + engineering. Automation and AI are core to how we work, not afterthoughts. WHO YOU’LL MARKET TO (ICP) * Fortune 500 / enterprise AI platform teams * AI-first companies (model/app builders) * Agentic companies (multi-agent products + automation) Stakeholders: Platform Eng, ML Eng/MLOps, Infra/DevOps, Data/AI leads, technical founders. We’ll provide the technical input; you’ll translate it into clear marketing. WHAT YOU’LL DO A) Collateral + messaging * One-pagers, decks, solution briefs, case studies, battlecards * Persona-based messaging + positioning updates B) Outbound (Email + LinkedIn) * Write/test sequences, iterate week...

...caching layers. ● Solid data engineering experience with Postgres, DynamoDB, and ClickHouse. ● Frontend engineering in React/ + TypeScript. NICE-TO-HAVE SKILLS ● Experience with Whisper, ElevenLabs, multimodal vision systems. ● Experience with GEPA-style prompt optimization loops. ● Experience with browser extension development for AI product workflows. ● Kubernetes familiarity and advanced MLOps methodologies. SUCCESS METRICS ● High reliability multi-agent workflows with automated recoverability. ● Efficient and deterministic RAG retrieval systems. ● Low-latency streaming architecture supporting real-time AI UI. ● Successful multi-LLM orchestration with cost reduction and fallback stability. ● Production-ready evaluation and regression testing frameworks. SUBMISSION REQUIREMENTS...

... REQUIRED TECHNICAL EXPERTISE LLM & NLP: • GPT, LLaMA, Claude, Gemini • RAG pipelines, embeddings, summarization Voice AI: • TTS (Azure, ElevenLabs, Coqui) • ASR (Whisper, NeMo) • SSML, voice cloning, audio DSP Video & Avatar AI: • FFmpeg automation, OpenCV, Whisper • Wav2Lip, SyncNet, avatar generation (D-ID, Synthesia) Computer Vision: • YOLO models, segmentation, OCR, moderation filters MLOps & Architecture: • Kubernetes, Docker, FastAPI • Model serving (Triton, TorchServe) • Vector DBs (Pinecone, Weaviate, FAISS) • Airflow, Temporal, CI/CD Backend Systems: • Distributed systems, microservices • REST & WebSocket services • AWS/GCP/Azure infrastructure PREFERRED QUALIFICATIONS &bull...

Phase I: Architecture Design and SetupThis phase establishes the core infrastructure for scalability and security. 1.1. Backend Infrastructure Setup (B1, B5, B9)Select and provision cloud resources (e.g., AWS, GCP, Azure). Set up the API gateway with load balancing. Define the /v1/liveness/verify endpoint. Set up object storage (e.g., S3, GCS) with mandatory AES-256 encr...Worker Fleet to ensure the target latency is met under peak Penetration Testing: Conduct a third-party security audit focusing on data transmission and storage Audit: Verify that the automated Data Retention Compliance mechanisms are correctly implemented and tested for scheduled : Final deployment to the production environment and transition to the MLOps monitoring phase.

...Build, maintain, and optimize MLOps pipelines for model training, deployment, monitoring, and retraining - Integrate Generative AI models into enterprise applications and workflows - Apply deep learning, neural networks, image processing, or conversational AI where needed - Act as the SME for AI/ML engineering practices, standards, and solution architecture - Collaborate with cross-functional teams to support end-to-end delivery - Ensure production systems meet reliability, scalability, and performance standards - Guide and mentor junior engineers and contribute to technical leadership - Support data engineering pipelines, data preparation, and model operationalization Required Qualifications - 5+ years of experience in AI/ML engineering, including 3+ years in MLOps - St...

I’m building an MLOps pipeline within a Non-Real-Time RAN Intelligent Controller (NON-RT RIC) environment to support intelligent network optimization. The goal is to automate the lifecycle of ML models—data ingestion, training, validation, deployment, and monitoring—using Kubeflow and KServe as core components. What I Need MLOps architecture design tailored to NON-RT RIC workflows. Integration of Kubeflow Pipelines for model training, retraining, and CI/CD automation. Configuration of KServe for scalable, production-grade model serving inside the RIC ecosystem. Assistance connecting the pipeline to network-metric sources and RIC policy output logic. Support in debugging model workflows, serving issues, containerization problems, or RIC integration gaps...

...surface and the concrete steps required to harden it from day one. You’ll help me: • Map potential threat vectors using frameworks such as MITRE ATLAS, OWASP for ML, or similar tools you trust (Adversarial Robustness Toolbox, CleverHans, Foolbox, etc.). • Produce an actionable risk-ranked report that details each vulnerability and the mitigation strategy, including any controls to embed in our MLOps pipeline. • Review (or co-create) high-level architecture diagrams, flagging weak points in data ingestion, training, inference, and model storage. • Recommend best practices for secure coding, access control, monitoring, and incident response specific to AI workloads. Acceptance criteria 1. Written assessment clearly lists each identified ris...

I’m building an Open RAN (O-RAN) solution and now need a production-ready MLOps pipeline around it. Kubeflow will orchestrate every workflow and KServe will handle model serving. The most critical pieces for me are model-training and model-deployment flows; CI/CD for the surrounding infrastructure is secondary. I already have several trained models that must be containerised and slotted straight into the new pipeline. Here’s what I expect to receive: • Infrastructure-as-code that spins up the required Kubernetes cluster on any major cloud provider, ready for Kubeflow and KServe • Kubeflow Pipelines covering data ingest, feature processing, training, validation, and artifact versioning • KServe endpoints with blue/green or canary rollout support so mo...

We are looking for an ML/AI Engineer with strong Python skills and hands-on experience deploying machine learning models in cloud environments. Requirements Excellent proficiency in Python and experience with Jupyter/Colab. Hands-on experience with MLOps tools: model versioning, deployment/orchestration, monitoring. Experience with cloud platforms (preferably AWS) and deploying ML models into production. English level: B2 or higher. Nice to Have Experience applying ML/AI to real product tasks (NLP, LLMs, generative models). Strong understanding of model inference, optimization techniques, and production-level deployment. Experience improving model efficiency: latency, throughput, caching, batching, compression, etc. Responsibilities Designing and optimizing ML model deve...

...experience Desired Skills: Excellent problem-solving and critical thinking skills with attention to detail in an ever-changing environment. Background in designing and implementing security mitigations and protections and/or publications in the space Currently participating in CTF/GRT/AI Red Teaming events and/or bug bounties developing or contributing to OSS projects. Understanding of ML lifecycle and MLOps. Perform various types of tests such as functional testing, regression testing, performance testing, and usability testing to evaluate the behavior and performance of the AI algorithms and models Ability to ensure the quality, consistency and relevance of data used for training and testing AI models (includes collecting, preprocessing and validating data) Ability to assess A...

I’m looking for a skilled engineer to create lightweight agents that can gather trace logs and key metrics across my entire stack. Scope • Cloud infrastructure: the agents must seamlessly collect data from AWS, Azure, and Google Cloud. • Containers: coverage is required for Docker, Kubernetes, and OpenShift environments. • AIOps / MLOps focus: the data pipeline has to capture performance metrics, logs and trace information, and resource-utilization figures so downstream analytic models receive complete, high-quality signals. What I need from you 1. Design and build installable agents (or sidecars) that auto-discover resources, stream data securely, and add minimal overhead. 2. Provide configuration options so I can enable or disable specific metric g...

...drawdowns, order/fill logs. * Alerts to email/Telegram/Slack. ## Nice-to-Have (Phase 2) * Options greeks & spreads; portfolio optimizer. * Alternative data (order book depth, options chain, social sentiment). * Strategy marketplace, multi-account orchestration. ## Tech Stack (Suggested) * **ML/Backend:** Python (FastAPI), Pandas/NumPy, scikit-learn, PyTorch/LightGBM, MLflow. * **Pipelines/MLOps:** Airflow/Prefect, Feast (feature store), Redis, Kafka (optional). * **DB/Storage:** PostgreSQL + TimescaleDB; object storage for artifacts. * **Frontend:** React/Next.js. * **Infra:** Docker, CI/CD, IaC (Terraform), AWS/GCP/Azure. ## Deliverables 1. Architecture + data/contracts + broker adapters. 2. **AI Algo Suite** (signals, scalping module, future-mover ranker) with noteb...

...building or consuming REST APIs at scale. - Familiarity with cloud AI tools like AWS Rekognition, Azure Face API, or Google Vision AI. - Understanding of Face Recognition fundamentals (Detection, Verification, Identification). - Comfortable working with Git and standard DevOps practices. Bonus Skills (Nice-to-Haves) - Experience with OpenCV or dlib for custom image processing. - Knowledge of MLOps or monitoring deployed AI systems. - Background in high-security or privacy-sensitive projects. - Understanding of Redis, Memcached, or similar caching systems. Why Work With Us You’ll be joining a tech-driven team working on a next-gen image tagging system powered by AI. We value clean architecture, speed, and privacy-conscious innovation — and you’ll have the fre...