Senior AI Engineer – Agentic Enterprise Runtime

@irsolutions

I bring over a decade of expertise in web and mobile development, specializing in AI and blockchain technologies. I understand the need for a Senior AI Engineer with the skills to build advanced AI infrastructure that integrates seamlessly into enterprise systems like ERP and CRM. Your project requires a real-time, context-aware decision runtime with explainable signal generation and adaptive feedback logic. In past projects, I have successfully implemented AI systems within enterprise platforms, orchestrated custom inference, and developed real-time decision engines. My experience also includes building explainability layers and feedback loops, ensuring robust multi-tenant API architecture. You can expect strong backend engineering, cloud-native architecture experience, and the ability to build adaptive AI systems post-deployment from me. I have a proven record of delivering scalable solutions that drive results in complex environments like the one you are envisioning. If you're looking for a seasoned AI engineer who can bring your vision to life, I am ready to collaborate. Let's discuss how we can bring your project to reality.

@corpmember29

HELLO, Your vision for an Agentic Enterprise Runtime aligns directly with my background in building modular AI reasoning systems embedded inside operational platforms — not chatbot wrappers. I specialize in: ✔ Custom inference orchestration & model routing ✔ Real-time signal/decision engines ✔ Explainability layers (confidence, trace logs, auditability) ✔ Human-in-the-loop feedback & adaptive retraining ✔ Multi-tenant, permission-scoped API architecture ✔ Cloud-native microservices systems Technical Approach Architecture Design • Event-driven reasoning layer (microservices-based) • Context injection per tenant/system • Model router (policy + ML hybrid orchestration) • Signal scoring + explainability module • Feedback capture service (overrides → retraining pipeline) • Monitoring & drift detection Preferred Stack Backend: Python (FastAPI) or Go AI Layer: PyTorch / ensemble models / hybrid ML + LLM reasoning Infra: Docker + Kubernetes Cloud: AWS/GCP Observability: Prometheus + structured telemetry I have experience building adaptive AI systems post-deployment with governance, monitoring, and enterprise-grade reliability. Available for long-term collaboration and architectural deep dives. Looking forward to discussing the runtime design in detail.

@juanjosec63

Hello, In my recent work, I architected and deployed an AI-driven decision runtime embedded inside a multi-tenant logistics ERP, processing ~2–5k requests/minute with model routing between XGBoost forecasting, anomaly detection models, and LLM-based reasoning, all orchestrated through a microservices layer on AWS (EKS + event bus). I designed the inference orchesation layer with confidence scoring, reasoning traces, structured audit logs, and a human-override feedback loop that fed suppression signals back into retraining pipelines; I can share architecture diagrams and private Git repositories under NDA detailing my exact backend and AI architecture role. The system exposed permission-scoped APIs with strict tenant isolation and policy-based access control, integrating directly into existing ERP workflows rather than operating as a chatbot overlay. My focus was on building a real-time signal engine with explainable outputs, model versioning, adaptive post-deployment learning, and cloud-native scalability using Kubernetes, Redis streams, and PostgreSQL. I’d be glad to provide technical documentation, system scale metrics, and walk through the architecture to assess alignment for your modular AI reasoning layer. Best regards, Juan

@OutsourceMan

⭐⭐⭐⭐⭐ We at CnELIndia, led by Raman Ladhani, have extensive experience designing and deploying enterprise-grade AI reasoning layers. Our portfolio includes AI systems fully embedded in ERP and CRM platforms, featuring custom multi-model inference orchestration, real-time decision engines, explainability layers with confidence scoring and audit logs, and adaptive feedback loops with human overrides. We excel in building modular, cloud-native microservices with multi-tenant, permission-scoped APIs capable of handling high request volumes. For this project, we can provide architecture diagrams, performance metrics, and code samples demonstrating scalable AI deployment. Our approach includes thorough system design, integration planning, and iterative validation, ensuring a reliable, explainable, and adaptive AI layer tailored to your enterprise workflows.

@Ahmedh058

Hi there, I’m Ahmed from Eastvale, California — a Senior Full-Stack & AI Engineer with over 15 years of experience building high-quality web and mobile applications. After reviewing your job posting, I’m confident that my background and skill set make me an excellent fit for your project — Senior AI Engineer – Agentic Enterprise Runtime . I’ve successfully completed similar projects in the past, so you can expect reliable communication, clean and scalable code, and results delivered on time. I’m ready to get started right away and would love the opportunity to bring your vision to life. Looking forward to working with you. Best regards, Ahmed Hassan

@shakilan4

Hi, I have built AI-driven enterprise systems including real-time decision engines, multi-model inference routing, and scalable microservice architectures integrated with ERP/CRM platforms. My experience includes explainable AI layers (confidence scoring, audit logs) and adaptive feedback workflows with permission-based multi-tenant APIs in production environments. I can share architecture details and relevant technical implementations demonstrating similar large-scale AI infrastructure work. Best regards, Shakila Naz

@Julian29

Hello, {{{ I HAVE CREATED SIMILAR BEFORE AND I CAN SHOW YOU }}}} I have reviewed your requirements and understand the need for a modular, real-time AI reasoning layer embedded into enterprise systems (ERP, CRM, WMS). I have 10+ years of experience in AI infrastructure, building multi-model inference engines, adaptive decision runtimes, explainability layers, and human-in-the-loop feedback systems for enterprise-scale platforms. I will provide 2 years free ongoing support and complete source code, follow Agile methodology, and assist from zero to production deployment. My experience includes cloud-native microservices, multi-tenant API architectures, real-time signal generation, audit logs, confidence scoring, and scalable orchestration of AI models across high-volume requests. I can share examples of prior AI systems embedded into enterprise platforms, architecture diagrams, and my exact role in each project. I am available for long-term collaboration and will ensure solutions are robust, maintainable, and production-ready. I eagerly await your positive response. Thanks

@hassansuhail

Hello ? Selected Relevant Work (Enterprise AI Systems): • Designed a modular AI decision layer embedded into a CRM + ERP stack (lead scoring + risk signals), serving ~12k users, ~40k daily inference calls, with confidence scoring + full audit logging. Role: system architect + backend lead. • Built a multi-model routing engine (OpenAI + Claude + internal classifier) with dynamic orchestration based on latency, cost, and task type. Included suppression logic + human override feedback loop stored in Postgres. • Developed a real-time signal engine for pipeline health (event-driven microservices, Kafka-based), generating explainable decision traces attached to CRM records. • Implemented permission-scoped, multi-tenant API layer (JWT + RBAC) deployed on Kubernetes with CI/CD + one-click rollout. Architecture: containerized microservices (Python/FastAPI + Node), event bus (Kafka), Postgres + Redis, vector layer where needed, modular agent orchestration (LangGraph-style), structured reasoning logs persisted per action. Explainability included confidence metrics, trace trees, and override capture for continuous retraining. I focus on production AI runtimes—not wrappers—with adaptive feedback loops and scalable cloud-native deployment. Happy to share deeper technical walkthroughs. Cheers, Hassan

@hr157

Hello, Below are relevant production systems I’ve architected and delivered — all embedded directly into enterprise platforms. I do not build chatbot wrappers; I build decision infrastructure. 1️⃣ Enterprise AI Runtime (CRM + ERP Embedded) Scale: ~15k+ users | ~2–4M inferences/month Role: Lead Architect & Backend Owner • Context-aware decision engine inside CRM workflows • Multi-model routing (classification + ranking + LLM) • Real-time opportunity scoring + suppression logic • Full audit trail with reasoning traces + confidence scoring • Human override feedback loop with adaptive recalibration • Multi-tenant RBAC API layer Architecture: Event-driven microservices (Node/Python), Kafka, PostgreSQL, Redis, Docker + Kubernetes. 2️⃣ Custom Inference Orchestrator (Multi-Model Routing) Scale: ~700–900 req/sec peak Role: Designed model router + versioned registry • Feature fingerprint-based model selection • Fallback chains + shadow deployments • Drift monitoring + retraining triggers • Tenant-scoped inference queues 3️⃣ Explainability & Governance Layer • Structured reasoning logs per decision • Feature contribution tracking • Confidence thresholds + escalation logic • Append-only audit store for compliance Technical Fit • Cloud-native microservices • Kubernetes-based scaling • Adaptive post-deployment learning loops • Permission-scoped APIs • Signal-based decision systems • Production monitoring + fail-safe design Let's discuss further

@natanr28

HELLO, HOPE YOU ARE DOING WELL! I understand that you are looking for an engineer to build a modular AI reasoning layer for enterprise systems, focusing on real-time decisions and adaptive feedback. My experience aligns perfectly with your needs. I have developed advanced AI systems embedded within ERP and CRM platforms, including custom inference orchestration and explainability layers with confidence scoring and audit logs. My plan involves delivering a detailed architecture diagram along with documentation of past projects that demonstrate real-time signal generation and multi-tenant API architecture. I'd like to have a chat with you at least so I can demonstrate my abilities and prove that I'm the best fit for this project. Warm regards, Natan.

@Hubready

Hello! I am a Florida-based senior software engineer with extensive experience in AI development, cloud computing, and backend systems. I've carefully reviewed your project description and am excited about the opportunity to contribute to the Agentic Enterprise Runtime. With over 15 years in the industry, I've built robust, scalable applications, integrating AI functionalities and microservices. I've successfully handled projects similar to yours, such as developing intelligent workflow automation systems and custom APIs. My experience includes: - An AI-powered dashboard for monitoring data analytics - A microservices architecture for an e-commerce platform - Integration of AI agents for automated customer service solutions Could you please clarify the following questions to help me better understand the project? 1. What specific AI functionalities are you looking to implement in the runtime? 2. Are there existing systems or platforms that the new AI components should integrate with? I believe that clear communication and structured milestones are key to a successful project. My goal is to ensure that the solutions are not only technically sound but also aligned with your business objectives. I’m committed to delivering production-ready code that meets your requirements. Let's connect to discuss how we can achieve your project goals together! - James

@moha262

Hello, I have thoroughly analyzed your project requirements and identified that my core competencies in AI Development, API Development, AI Agents, Backend Development, AI Consulting, Microservices, AI Model Development and Cloud Computing align precisely with your technical specifications. With a decade of specialized experience in this domain, I bring proven expertise in implementing industry-standard methodologies and delivering production-ready solutions. My approach involves: - Technical Assessment: Comprehensive evaluation of project scope, technical constraints, and deliverable requirements - Strategic Planning: Development of detailed implementation roadmap with milestone tracking - Quality Assurance: Rigorous testing protocols and quality control measures throughout the development lifecycle - Timely Delivery: Proven track record of meeting deadlines while maintaining exceptional quality standards I am particularly adept at handling complex technical challenges and implementing scalable solutions that exceed client expectations. My methodology emphasizes precision, efficiency, and adherence to best practices in AI Development, API Development, AI Agents, Backend Development, AI Consulting, Microservices, AI Model Development and Cloud Computing. I would welcome the opportunity to discuss your project's specific technical requirements, timeline expectations, and success metrics in greater detail. Please feel free to schedule a consultation at your convenience. Thank y

@DngPhgNam

⭐⭐⭐⭐⭐ ✅Hi there, hope you are doing well! I have successfully engineered AI systems embedded into enterprise platforms involving real-time decision engines and multi-model orchestration that seamlessly handled adaptive feedback and explainability layers. From my experience, the most crucial part to delivering success on this project is designing a robust modular AI runtime capable of real-time context-aware decision-making with clear auditability and human override feedback. Approach: ⭕ I will review your existing enterprise environment for integration points. ⭕ Architect and develop a microservices-based AI reasoning runtime with multi-model routing. ⭕ Build explainability components including confidence scoring and audit logs. ⭕ Implement adaptive feedback loops using human overrides. ⭕ Ensure a scalable cloud-native design with multi-tenant API security. ❓ Could you share any existing architecture diagrams or technical documentation? ❓ What scale of users and requests per second do you expect initially? ❓ Are there preferred tech stacks or cloud providers for this project? I am confident my strong backend, AI infrastructure experience, and cloud-native microservices development will drive a robust, scalable solution for your enterprise AI runtime. Thanks, Nam

@vudinhgiang

Hello, I previously led backend architecture for an AI decision engine embedded inside a logistics ERP, where we built a real-time signal layer (Python microservices + event bus) that performed multi-model routing, generated confidence-scored recommendations, logged reasoning traces for auditability, and supported human override with suppression feedback loops; the system handled ~40K events/day across multi-tenant clients, and I was responsible for the orchestration layer, inference routing, and permission-scoped API design. I’ve also worked on a CRM-embedded revenue intelligence module with explainability logging, adaptive post-deployment retraining signals, and modular cloud-native deployment (Docker + Kubernetes), and I can share architecture diagrams and private repo walkthroughs detailing scale, request flow, and my exact engineering ownership. If you're looking for someone who understands runtime decision systems, not just LLM wrappers, I’d be glad to discuss your reasoning layer architecture and how I can contribute long-term. Best regards, Giang

@ayesha86664

Hi there! I understand you are looking for an AI engineer to build a modular reasoning layer, not just integrate an LLM. Embedding real-time, context-aware decision logic into enterprise systems with explainability is a highly specialized requirement. I have experience developing AI inference pipelines and adaptive reasoning engines for enterprise platforms. I’ve worked on multi-model orchestration, real-time signal processing, and feedback loops with human oversight. I also have implemented permission-scoped APIs and explainability layers, including confidence scoring and audit trails. My approach will be to design a cloud-native, modular system that integrates directly into your ERP/CRM/WMS stack. The solution will provide real-time decisions with adaptive feedback, auditability, and scalable multi-tenant support. Architecture will be clearly documented, and all components will follow microservices best practices. check our work https://www.freelancer.com/u/ayesha86664 Do you want the initial design to focus on one platform first or multiple enterprise systems simultaneously? Let me know if you’re interested & we can discuss it. Best Regards Ayesha

@jorginhoo1

Hello, I’ve built advanced AI infrastructure embedded in enterprise systems, including ERP and finance platforms, with real-time decision engines, multi-model routing, and explainable outputs. For example, I developed an AI module in an ERP handling live inventory and financial data across tenants, producing predictive alerts with confidence scores and reasoning traces. The system supported human feedback loops, multi-tenant APIs, and hundreds of requests per second. I contributed to backend microservices, inference orchestration, and secure, modular architecture. Architecture diagrams and GitHub examples are available on request. I understand you need a context-aware AI runtime—not a simple LLM wrapper—with explainable signals, adaptive feedback, and secure multi-tenant handling. I’d implement microservices that route inputs to specialized models, capture overrides, generate audit logs, and maintain full scalability and security. Do you prefer integration with all enterprise systems at once or a phased approach per platform? I’m eager to contribute long-term and help build a robust, production-grade AI reasoning layer embedded into your enterprise platforms. Thank you.

@mugencodes

Hello, I am Jayabrata Bhaduri, a Project Manager with a skilled team specializing in API Development, AI Model Development, and AI Development. We have successfully delivered numerous projects in these areas and are well-equipped to handle your requirements for the Senior AI Engineer position at Agentic Enterprise Runtime. I have thoroughly reviewed your project description and understand the need for a modular AI reasoning layer embedded in enterprise systems. Our team excels in building advanced AI infrastructure, including custom inference orchestration, real-time signal engines, explainability layers, and multi-tenant API architecture. I would love to discuss your project further to provide a tailored solution that meets your specific needs. Please feel free to schedule a call at your convenience. Regards, Jayabrata Bhaduri

@matheussilva114

Hi, I have read your project details and I know what you expect. I am a senior engineer with over 6 year of experience on Cloud Computing, Backend Development, API Development, Microservices, AI Consulting, AI Model Development, AI Development, AI Agents. Please visit my profile to view my latest projects, certificates, and work history. Let's connect in chat to discuss more. Regards, Matheus

@Alesha1018

Hello, hope you are doing well, As a seasoned AI and backend developer with a fine-tuned appreciation for enterprise architecture, I'm equipped with relevant experience in building advanced AI infrastructure. This particular project aligns profoundly with my past achievements in embedding purposeful AI systems into various enterprise platforms such as ERPs, CRMs, WMS and proprietary ones. The entire setup would require custom inference orchestration, multi-model routing, robust explainability layers and adaptive feedback loops – domains which I have excelled in over years. Looking at the comprehensive tech expectations of this assignment, it's clear that you need someone who can blend meticulous-backend engineering with cloud-native architecture experience. This is my forte. My proactive nature ensures smooth sailing in complex projects and my ability to think long-term will set-up a robust system that handles every possible situation post-deployment.