Senior AI Engineer – Agentic AI Platform

Hudson Manpower • Cincinnati, OH

2d

About The Position

We are seeking a Senior AI Engineer – Agentic AI Platform to design and build enterprise-scale Agentic AI platforms that enable multiple business domains to develop, deploy, monitor, govern, and operate autonomous AI agents. This role requires strong hands-on experience in Agentic AI, multi-agent orchestration, AI platform architecture, model governance, memory management, observability, cost attribution, and cloud-native AI solutions. The ideal candidate will have production experience with Azure AI Foundry, Azure OpenAI, LangChain, LangGraph, Python, Azure, vector databases, API gateways, and enterprise AI engineering practices.

Requirements

• 8–10 years of software engineering or platform engineering experience.
• 3+ years of hands-on AI/ML or Generative AI experience.
• Production experience building enterprise-scale AI applications.
• Strong experience designing AI architectures and platforms, not only individual AI applications.
• Hands-on experience with Agentic AI / AI Agents.
• Strong experience with Azure.
• Hands-on experience with Azure AI Foundry.
• Hands-on experience with Azure OpenAI.
• Strong experience with LangChain and/or LangGraph.
• Strong Python development experience.
• Experience with multi-agent orchestration and agentic workflow patterns.
• Experience with RAG, vector databases, AI memory, and agent state management.
• Experience with REST APIs and API gateways, preferably Azure API Management (APIM).
• Experience with event-driven architectures and messaging systems.
• Experience with AI monitoring, observability, governance, and cost/token usage tracking.
• Experience working with enterprise data/storage technologies such as Cosmos DB, PostgreSQL, MongoDB, or vector databases.
• Experience with SQL.
• Experience designing scalable, secure, and governed AI platforms.

Nice To Haves

• Semantic Kernel
• Model Context Protocol (MCP)
• C# / .NET
• Kafka
• Azure Service Bus
• Azure Event Grid
• Azure Durable Functions
• Neo4j, Stardog, Amazon Neptune, or other graph databases
• Enterprise knowledge graphs
• Ontology-driven AI solutions
• AI FinOps and chargeback/showback
• Responsible AI frameworks
• AI evaluation and benchmarking
• AWS or GCP
• Experience in healthcare, financial services, insurance, or other regulated industries

Responsibilities

• Agentic AI Development: Design and develop sophisticated multi-agent AI systems for enterprise use cases. Build autonomous and semi-autonomous AI workflows. Implement Supervisor-Worker, Sequential, ReAct, Planner-Executor, Writer-Critic, orchestration, and choreography patterns. Develop scalable agent communication and execution frameworks. Build closed-loop workflows with validation, retry, evaluation, and feedback mechanisms.
• Enterprise AI Platform Engineering: Build reusable AI platform capabilities for multiple business teams. Design enterprise AI governance and operational controls. Develop API-driven AI services supporting rate limiting, quota management, authentication, authorization, audit logging, multi-tenant usage tracking, and cost attribution. Establish agent onboarding and lifecycle management capabilities.
• Multi-Agent Orchestration: Design agent communication using direct calls, event-driven architectures, message queues, and publish-subscribe patterns. Implement orchestration and choreography-based execution models. Work with Kafka, Azure Service Bus, Azure Durable Functions, and event-driven workflows.
• AI Memory & Knowledge Systems: Design short-term and long-term AI memory architectures. Implement vector databases, semantic caching, conversation memory, agent state persistence, and RAG. Build knowledge orchestration frameworks supporting agent collaboration.
• Ontology & Knowledge Graphs: Work with graph databases and enterprise knowledge models. Support ontology-driven AI applications. Build knowledge graphs for relationship-based reasoning. Integrate structured, unstructured, and graph-based knowledge sources.
• AI Governance & FinOps: Implement AI consumption governance across business domains. Track token usage, model consumption, API utilization, and operational costs. Develop chargeback/showback mechanisms. Support AI FinOps reporting and capacity planning.
• Reliability & Observability: Design observability frameworks for AI applications. Monitor agent execution, tool usage, latency, hallucinations, failure rates, and model quality. Build dashboards and operational metrics for AI workloads.
• Responsible AI & Security: Implement guardrails, safety controls, prompt protection, data masking, PII protection, and human-in-the-loop validation. Ensure compliance with enterprise security and governance requirements. Build secure Agentic AI systems handling sensitive business data.
• AI Evaluation & Optimization: Develop agent and tool evaluation frameworks. Measure response quality and detect hallucinations. Implement closed-loop evaluation mechanisms. Apply context engineering, prompt engineering, retrieval optimization, agent tuning, and AI benchmarking.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed