Senior AI Engineer – Agentic AI Platform

Hudson Manpower is hiring a Senior AI Engineer to help design and deliver an enterprise-scale agentic AI platform. This onsite role in Cincinnati, OH focuses on building the capabilities needed to create, deploy, monitor, govern, and operate autonomous AI agents across business domains, with a cloud-native approach on Azure.

The work spans multi-agent orchestration patterns, AI platform architecture, governance and FinOps controls, memory and RAG, observability and evaluation, and responsible AI guardrails for sensitive, regulated enterprise contexts.

What you will do

• Design and develop multi-agent AI systems for enterprise use cases.
• Build autonomous and semi-autonomous AI workflows.
• Implement agent orchestration and workflow patterns including Supervisor-Worker, Sequential, ReAct, Planner-Executor, and Writer-Critic, including orchestration and choreography approaches.
• Develop scalable frameworks for agent communication and execution.
• Create closed-loop workflows that include validation, retry, evaluation, and feedback mechanisms.
• Build reusable AI platform capabilities that multiple business teams can use.
• Design enterprise AI governance and operational controls for agent operations.
• Develop API-driven AI services with operational and security features such as rate limiting, quota management, authentication, authorization, audit logging, multi-tenant usage tracking, and cost attribution.
• Establish agent onboarding and lifecycle management capabilities.
• Implement agent communication using direct calls, event-driven architectures, message queues, and publish-subscribe patterns.
• Work with Kafka and Azure event tooling including Azure Service Bus and Azure Durable Functions to support event-driven workflows.
• Design short-term and long-term AI memory architectures, including vector databases, semantic caching, conversation memory, and agent state persistence.
• Build knowledge orchestration frameworks for agent collaboration, including support for graph databases and enterprise knowledge models.
• Support ontology-driven AI applications and implement knowledge graphs for relationship-based reasoning.
• Integrate structured, unstructured, and graph-based knowledge sources.
• Implement AI consumption governance across business domains, including token usage tracking and operational cost monitoring.
• Develop chargeback/showback mechanisms and support AI FinOps reporting and capacity planning.
• Design observability for AI applications, including monitoring agent execution, tool usage, latency, hallucinations, failure rates, and model quality.
• Build dashboards and operational metrics for AI workloads.
• Implement guardrails and safety controls including prompt protection, data masking, PII protection, and human-in-the-loop validation.
• Ensure compliance with enterprise security and governance requirements, including secure agentic AI systems for sensitive business data.
• Develop agent and tool evaluation frameworks, including response quality measurement and hallucination detection, and implement closed-loop evaluation mechanisms.
• Apply context engineering, prompt engineering, retrieval optimization, agent tuning, and AI benchmarking.

Minimum 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, including Azure AI Foundry and 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 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.

Useful technologies

• Azure, Azure AI Foundry, Azure OpenAI, LangChain, LangGraph, Python
• Vector databases, RAG, REST APIs, API gateways, Azure API Management (APIM)
• Kafka, Azure Service Bus, Azure Durable Functions
• Message queues, publish-subscribe patterns, event-driven architectures
• Cosmos DB, PostgreSQL, MongoDB, SQL

Desirable skills

• Semantic Kernel
• Model Context Protocol (MCP)
• C# / .NET
• Azure Event Grid
• Graph databases such as Neo4j, Stardog, Amazon Neptune, or other graph databases
• Enterprise knowledge graphs and ontology-driven AI solutions
• AI FinOps and chargeback/showback, responsible AI frameworks, and AI evaluation and benchmarking
• AWS or GCP experience
• Experience in healthcare, financial services, insurance, or other regulated industries

Location: Cincinnati, OH (onsite). Compensation: USD 55–60 per hour.