Software Engineer

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Efficient Workflows for Training Large Language Models ❇️ Hi My Friend, I hope you're doing well. I reviewed your project requirements and see you are looking for a software engineer to build tooling and workflows for large language models. You don't need to look any further; Zohaib is here to assist you! My team has completed over 50 similar projects in developing data pipelines and workflows. I will create efficient data pipelines and refine datasets to enhance model evaluations. ➡️ Why Me? I can easily handle your project as I have 5 years of experience in building data pipelines, refining datasets, and analyzing model performance. My expertise includes Python programming, data processing, and model evaluation. Additionally, I have a strong grip on deploying models in production and ensuring high-quality results. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Python Programming ✅ Data Pipeline Development ✅ Dataset Refinement ✅ Model Evaluation ✅ Data Analysis ✅ Workflow Automation ✅ Attention to Detail ✅ Problem Solving ✅ API Integration ✅ Machine Learning ✅ LLM Deployment ✅ Startup Experience Waiting for your response! Best Regards, Zohaib

@thewebscraper

Dear Client, Being a proficient and experienced Software Engineer for over 14 years, I believe I can cater to your requirements skillfully. My expertise includes working with Amazon Web Services, Python and API Integrations, which aligns perfectly with your project demands. My portfolio boasts extensive experience in building data pipelines and automating processes, which were major contributors to my reputed client relations with organizations like Amazon Web Services and Google Maps. In addition to that, my forte also lies in Web Scraping; an important aspect of data pipeline generation for training and evaluation purposes in large language models. As you seek someone who is comfortable in working remotely and is familiar with professional workflows, my vast experience of handling remote projects perfectly complements this need. I am diligent, focused on details and committed to delivering my best. Not just a pitch, but a promise that with me on board, you will receive top-quality work, within the set timeline. Hiring me will ensure that all your responsibilities are thoroughly taken care of and the purpose of tooling and workflows creation for complex training

@Ekarthaan

Hi maureenkiambi0, I recently completed a similar project. Here are links to my website where you will find similar jobs done recently by us. I have written my suggestions and question too for you to consider. Thank you for considering me for your software engineering project! Your need for building data pipelines to train AI models resonates with my experience in Python and production deployment. Here's a streamlined approach for your project: - Refine existing datasets and translate them into evaluation signals for AI models - Analyze model failure points using real-world workflows I'm eager to learn more about your project. Could you provide insight into the specific AI models you aim to evaluate? Additionally, have you considered implementing continuous integration/continuous deployment (CI/CD) practices for your evaluation pipelines to streamline model updates? Based on my background, I suggest leveraging version control systems like Git for data tracking and exploring containerization using Docker for seamless deployment. Looking forward to potentially collaborating on this exciting venture! Best Regards, Sid (Ceo and Founder of Ekarthaan India)

@vorasiddh4it

Hello Maureenkiambi.. We specialize in end-to-end AI data engineering and model evaluation pipelines, currently delivering tooling that processes large-scale, human-curated datasets into structured training & evaluation signals for frontier LLMs. With ready-to-use Python frameworks and scalable ETL templates. Our workflow includes data ingestion from multiple formats, automated preprocessing, dataset labeling/enrichment, evaluation pipeline creation, and detailed failure-point analytics. We build LLM fine-tuning & evaluation workflows that integrate seamlessly into your existing AI stack. ✅ Core Techniques & Tools: ➡️ Python (Pandas, Dask, PySpark) for big data preprocessing & transformation ➡️ LLM integration with OpenAI API, Hugging Face Transformers, LangChain ➡️ Automated evaluation pipelines for response quality, factual accuracy & bias detection ➡️ Data mining & enrichment with NLP + prompt engineering ➡️ Scalable deployment on cloud (AWS/GCP) with containerization (Docker) ✅ Relevant Projects: ➡️ Built automated pipelines to score model outputs on accuracy, coherence, and ethical compliance ➡️ Transformed expert-annotated text into structured training datasets for GPT-style models ➡️ Created analytics dashboard to track & visualize LLM weaknesses in professional workflows ✅ With 12+ years in AI/ML engineering, plus ready LLM data processing templates, I can deliver robust, scalable pipelines that accelerate model training & evaluation.

@liamch3D

Hello maureenkiambi0, I’ve been working with Python, Machine Learning (ML), Data Mining, Big Data Sales, Hadoop, AI Model Development, AI Model Integration, AI Development for over 7 years, and I love helping people bring their ideas to life. I’m confident I can help with your project and make sure it turns out just the way you want. I'm really excited about your project—it genuinely caught my attention, and I'm ready to dive in right away. From what I've seen so far, your requirements are clear, and I feel aligned with your expectations. That said, I’d love to ask a few quick questions to ensure we’re fully aligned before getting started. I’m excited to work with you and make this project awesome! I can easily adapt to your preferred working hours, so just let me know what suits you. Talk soon, Cheers Liam

@Microlent

Hello, I build data tooling and evaluation workflows for LLMs and can contribute immediately on a part-time basis (10–40 hrs/week) for 4 weeks, with the option to extend. What I’ll deliver - Training data pipelines: Python-first ETL with schema validation, dataset versioning, deduplication, PII redaction, and quality gates; supports large, heterogeneous sources and produces clean, traceable corpora ready for model ingestion.[1] - Expert dataset refinement → eval signals: Convert SME rubrics into structured tasks and JSON schemas; generate golden and adversarial sets; define per-task scorers (exact/semantic match, reasoning steps, tool-use success), and difficulty tiers for granular evaluation. - Failure analysis on real workflows: Scenario-driven eval harnesses mirroring production usage; error taxonomy (reasoning, grounding, tool-use, policy, style) with KPIs, and dashboards that surface regressions, drift, and blind spots for targeted remediation. Relevant portfolio experience Proposed week-1 plan - Align on priority workflows and error taxonomy. - Implement v0 evaluation harness with 5–8 core tasks and scorers. - Stand up dataset versioning and validation checks. - Deliver first failure-analysis report with prioritized fixes. Happy to start with a short paid pilot and share a concrete plan tailored to your domain. Best Regards Rajesh

@dhanjibharwad17

Hi there, You’re looking for a software engineer to build tooling, workflows, and evaluation pipelines for large language models. We’ve worked on similar AI projects involving data pipeline creation, dataset refinement, and model evaluation in real-world workflows. Key Questions: Will the LLM integration be with an existing model (e.g., OpenAI API) or an in-house model? What data sources/formats should the pipelines handle initially? Team Setup: 1 Python/ML Engineer (data pipelines, evaluation tooling) 1 AI Engineer (LLM deployment, fine-tuning, and integration) 1 QA/Data Analyst (dataset validation & analysis of model outputs) Milestones: 1: Understand current LLM setup and evaluation goals 2: Build and test data pipelines for training/evaluation 3: Refine datasets and transform into evaluation signals 4: Analyze model failure cases and optimize workflows 5: Final reporting and handover Methodology: Agile approach with weekly deliverables, clear documentation, and version-controlled code for smooth collaboration. Portfolio Samples: Custom LLM fine-tuning and deployment for a domain-specific knowledge base Python-based ETL pipelines for processing multi-source training datasets Evaluation framework for NLP models with detailed failure analysis Budget & Timeline: Finalized after confirming the full scope and engagement hours. Next Step: Let’s discuss your model setup, data sources, and priority workflows so we can align on the perfect plan. Thanks,

@digitrixsoftware

Hi, I’m Bhavik Patel, a Python engineer with hands-on experience building LLM data pipelines, evaluation tooling, and workflow automation for AI model training. I’ve worked on deploying and evaluating LLMs in production, refining datasets, and generating insights from model failure analyses. What I can deliver: Scalable data pipelines for training and evaluation. Refinement of expert-labeled datasets into structured evaluation signals. Automated failure point analysis and reporting for real-world workflows. Integration with model deployment workflows for continuous improvement. Best regards, Bhavik Patel

@technosystem

I believe I am the perfect fit for this position as a Software Engineer with a strong background in data pipelines, machine learning, and Python. Throughout my career, I have contributed significantly to various industries through my mobile and web app development expertise. I have gained experience working on complex projects similar to yours, producing scalable websites, eCommerce solutions and even blockchain-based platforms. As an entrepreneur myself, I understand the importance of creating efficient workflows and delivering high-quality results. This resonates with your need to build tooling and workflows for generating training and evaluation data for language models. My fluency in Python has been a significant strength in deploying systems in production, including evaluation pipelines like the one you're seeking assistance with. My experience extends to data mining as well -- a critical component of shaping the human expert-created datasets into valuable signals for evaluating models. Paired with my dedicated attention to detail, strong problem-solving skills, and commitment to learning, I am confident that we can together deliver analyses of model failure points on professional workflows based on the evaluation results. Let's use our mutual expertise to not just create marginally satisfactory products, but truly frontier AI models!