What are the responsibilities and job description for the Senior AI Engineer - Agentic AI Platform position at Hudson IT?
Role Overview
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.
Key Responsibilities
Agentic AI Development
Enterprise AI Platform Engineering
Multi-Agent Orchestration
AI Memory & Knowledge Systems
Ontology & Knowledge Graphs
AI Governance & FinOps
Reliability & Observability
Responsible AI & Security
AI Evaluation & Optimization
Mandatory Qualifications
Desirable Skills
Mandatory Qualifications
Desirable Skills
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.
Key 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.
Mandatory Qualifications
- 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.
Desirable Skills
- 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 Google Cloud Platform
- Experience in healthcare, financial services, insurance, or other regulated industries
Mandatory Qualifications
- 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.
Desirable Skills
- 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 Google Cloud Platform
- Experience in healthcare, financial services, insurance, or other regulated industries