Senior AI Engineer – Agentic AI Platform

Senior Ai Engineer – Agentic Ai Platform

Design and build an enterprise-scale Agentic AI platform. Enable multiple business domains to develop, deploy, monitor, and 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.

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, and feedback loops.

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, and audit logging. Establish structured onboarding and lifecycle management for AI agents.

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.

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.

Implement AI consumption governance across business domains. Track token usage, model consumption, API utilization, operational costs. Develop chargeback/showback mechanisms. Support AI Fin Ops reporting and capacity planning. Implement cost optimization strategies for enterprise AI workloads.

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.

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.

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.

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.

Azure AI Foundry, Azure OpenAI, Lang Chain, Lang Graph, Semantic Kernel — preferred, MCP / Model Context Protocol, Generative AI, Agentic AI, Multi-Agent Systems, RAG.

Microsoft Azure — Required, GCP — Plus, AWS — Plus.

API gateways, AI governance platforms, Azure API Management (APIM), REST APIs, event-driven systems, multi-tenant AI architectures.

Python — Required, C# / .NET — Preferred, SQL.

Cosmos DB, PostgreSQL, MongoDB, vector databases, graph databases, Neo4j, Stardog, Amazon Neptune.

Kafka, Azure Service Bus, Azure Event Grid, Azure Durable Functions, message queues, event-driven architecture.

AI observability, monitoring and…