Senior AI Platform Engineer – Agentic AI

Position Summary

We are seeking a highly skilled Senior AI Engineer to help design and build an enterprise-scale Agentic AI platform that enables multiple business domains to develop, deploy, monitor, and govern autonomous AI agents. This role goes beyond traditional LLM application development and requires hands-on expertise in agent orchestration, AI platform architecture, model governance, memory management, observability, cost attribution, multi-agent systems, and scalable cloud-native AI solutions. The ideal candidate will have experience building production-grade AI systems using Azure AI Foundry, LangChain, LangGraph, vector databases, API gateways, and modern AI engineering practices. The individual should be comfortable making architecture decisions, evaluating technology trade-offs, and designing enterprise-ready solutions that support security, scalability, monitoring, and cost control.

Key Responsibilities

• Agentic AI Solution Development
• Build sophisticated multi-agent AI systems for enterprise use cases.
• Implement supervisor-worker, sequential, orchestration, choreography, ReAct, Planner-Executor, and Writer-Critic agent architectures.
• Develop scalable agent communication and execution frameworks.
• Design closed-loop AI workflows with validation, retry, evaluation, and feedback mechanisms.
• Enterprise AI Platform Engineering
• Build reusable AI platform capabilities consumed by multiple business teams.
• Implement enterprise-grade AI governance and operational controls.
• Design API-driven AI Service Architecture With: Rate limiting, Quota management, Multi-tenant usage tracking, Cost attribution, Authentication & authorization, Audit logging.
• Enable structured onboarding and lifecycle management of AI agents.
• Multi-Agent Orchestration
• Design Orchestration Frameworks Where Agents Communicate Through: Direct calls, Event-driven architectures, Message queues, Publish-subscribe patterns.
• Evaluate technologies such as Kafka, Azure Durable Functions, Service Bus, and event-driven workflows.
• AI Memory & Knowledge Systems
• Design short-term and long-term memory architectures.
• Implement: Vector databases, Semantic caching, Conversation memory, Agent state persistence, Retrieval-Augmented Generation (RAG).
• Develop knowledge orchestration frameworks supporting agent collaboration.
• Ontology & Graph-based Intelligence
• Work with graph databases and enterprise knowledge models.
• Support ontology-driven AI applications.
• Build knowledge graphs that enable relationship-based reasoning and signal generation.
• Design systems that combine structured, unstructured, and graph-based knowledge sources.
• Model Governance & FinOps
• Implement AI consumption governance across business domains.
• Track: Token usage, Model consumption, API utilization, Operational costs.
• Create chargeback/showback mechanisms for enterprise teams.
• Support AI FinOps reporting and capacity planning.
• Reliability, Monitoring & Observability
• Design observability frameworks for AI applications.
• Monitor: Agent executions, Tool usage, Latency, Hallucinations, Failure rates, Model quality.
• Create dashboards and operational metrics for enterprise AI workloads.
• Responsible AI & Security
• Implement: Guardrails, Safety controls, Prompt protection, Data masking, PII protection, Human-in-the-loop validation.
• Ensure compliance with enterprise security and governance policies.
• Build secure agentic systems 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 Including: Context engineering, Prompt engineering, Retrieval optimization, Agent tuning, AI system benchmarking.

Qualifications

• 7+ years in software engineering or platform engineering.
• 3+ years building AI/ML or Generative AI solutions.
• Experience delivering enterprise-scale production AI applications.
• Experience designing AI architectures rather than only building individual AI applications.
• Technical Skills
• Generative AI & Agentic Frameworks
• Azure AI Foundry
• Azure OpenAI
• LangChain
• LangGraph
• Semantic Kernel (preferred)
• MCP (Model Context Protocol)
• Cloud Platforms
• Microsoft Azure (required)
• Experience with GCP or AWS is a plus
• Enterprise Integration
• API gateways and AI governance platforms
• Azure API Management (APIM)
• REST APIs
• Event-driven systems
• Programming
• Python (required)
• C# (.NET) preferred
• SQL
• Data & Storage
• Cosmos DB
• PostgreSQL
• MongoDB
• Vector databases
• Graph databases (Neo4j, Stardog, Neptune, etc.)
• Messaging & Streaming
• Kafka
• Azure Service Bus
• Event Grid
• Durable Functions
• AI Operations
• AI observability
• Monitoring & logging
• Token usage analysis
• Cost optimization
• Model lifecycle management

Preferred Qualifications

• Experience implementing ontology-driven solutions.
• Experience with enterprise knowledge graphs.
• Experience building autonomous AI systems.
• Experience with AI governance and responsible AI frameworks.
• Experience designing reusable AI platforms used by multiple business units.
• Experience with healthcare, financial services, insurance, or regulated industries.