Senior AI Engineer – Agentic AI Platform

Job Details

Position Summary

Design and build an enterprise-scale Agentic AI platform. Enable multiple business domains to: Develop AI agents, Deploy AI agents, Monitor AI agents, Govern AI agents. Focus on enterprise AI platform engineering rather than basic LLM application development. Build production-grade AI systems with emphasis on: Agent orchestration, AI platform architecture, Model governance, Memory management, Observability, Cost attribution, Multi-agent systems, Cloud-native architecture, Security and scalability.

Agentic AI Solution Development

Design and develop sophisticated multi-agent AI systems. Build autonomous and semi-autonomous AI workflows. Implement agent architectures including: Supervisor-worker, Sequential Orchestration, Choreography, ReAct, Planner-Executor, Writer-Critic. Develop scalable agent communication and execution frameworks. Design closed-loop AI workflows with: Validation, Retry mechanisms, Evaluation, Feedback loops.

Enterprise AI Platform Engineering

Build reusable AI platform capabilities for multiple business teams. Implement enterprise AI governance and operational controls. Design API-driven AI services with: Rate limiting, Quota management, Multi-tenant usage tracking, Cost attribution, Authentication and authorization, Audit logging. Establish structured onboarding and lifecycle management for AI agents.

Multi-Agent Orchestration

Design agent communication through: Direct API calls, Event-driven architectures, Message queues, Publish-subscribe patterns. Implement: Choreography-based execution, Conductor/orchestrator-based execution. Evaluate and utilize technologies such as: Kafka, Azure Durable Functions, Azure Service Bus, 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, RAG. Develop knowledge orchestration frameworks supporting agent collaboration. Work with graph databases and enterprise knowledge models. Support ontology-driven AI applications. Build knowledge graphs for: Relationship-based reasoning, Signal generation, Knowledge discovery. Combine: Structured data, Unstructured data, Graph-based knowledge.

Model Governance & FinOps

Implement AI consumption governance across business domains. Track: Token usage, Model consumption, API utilization, Operational costs. Develop chargeback/showback mechanisms. Support AI FinOps reporting and capacity planning. Implement cost optimization strategies for enterprise AI workloads.

Reliability, Monitoring & Observability

Design observability frameworks for AI applications. Monitor: Agent executions, Tool usage, Latency, Hallucinations, Failure rates, Model quality. Build dashboards and operational metrics for AI workloads. Implement comprehensive AI monitoring and logging.

Responsible AI & Security

Implement: AI guardrails, Safety controls, Prompt protection, Data masking, PII protection, Human-in-the-loop validation. Ensure compliance with enterprise security and governance policies. Design secure agentic systems capable of handling sensitive business data.

AI Evaluation & Optimization

Develop frameworks for: Agent evaluation, Tool evaluation, Response quality measurement, Closed-loop evaluation, Hallucination detection. Apply advanced AI engineering techniques: Context engineering, Prompt engineering, Retrieval optimization, Agent tuning, AI benchmarking.

Required Qualifications

• 7+ years of software engineering or platform engineering experience.
• 3+ years building AI/ML or Generative AI solutions.
• Experience delivering enterprise-scale production AI applications.
• Experience designing AI architectures, not just individual AI applications.
• Strong architecture and technology trade-off decision-making skills.
• Experience implementing ontology-driven solutions.
• Enterprise knowledge graph experience.
• Experience building autonomous AI systems.
• Experience with AI governance and responsible AI frameworks.
• Experience designing reusable AI platforms consumed by multiple business units.
• Experience in regulated industries such as: Healthcare, Financial Services, Insurance.

Preferred Qualifications

• Experience implementing ontology-driven solutions.
• Enterprise knowledge graph experience.
• Experience building autonomous AI systems.
• Experience with AI governance and responsible AI frameworks.
• Experience designing reusable AI platforms consumed by multiple business units.
• Experience in regulated industries such as: Healthcare, Financial Services, Insurance.

Essential Skills

• Senior AI Engineer
• Agentic AI Engineer
• Agentic AI Solutions Architect
• AI Platform Engineer
• Generative AI Engineer
• AI Solutions Architect
• AI Agents
• Multi-Agent Systems
• Generative AI
• Azure AI Foundry
• Azure OpenAI
• LangChain
• LangGraph
• Python
• RAG
• Vector Database
• Knowledge Graph
• Ontology
• Azure APIM
• Azure API Management (APIM)
• API Gateways
• Distributed Systems
• Event-Driven Architecture
• AI FinOps
• Model Governance