Senior AI Engineer – Agentic AI Platform

Role Overview

Agentic AI Development

• Design and develop 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.
• Build closed-loop workflows with validation, retry, evaluation, and feedback mechanisms.

Platform Engineering

• Build reusable AI platform capabilities for multiple business teams.
• Design enterprise AI governance and operational controls.
• Develop API-driven AI services with rate limiting, quota management, authentication, authorization, audit logging, multi-tenant usage tracking, and cost attribution.
• Establish agent onboarding and lifecycle management capabilities.

Orchestration and Knowledge

• Design agent communication using direct calls, event-driven architectures, message queues, and publish-subscribe patterns.
• Work with Kafka, Azure Service Bus, and Azure Durable Functions.
• Design short-term and long-term AI memory, semantic caching, conversation memory, agent state persistence, and RAG.
• Build knowledge graphs and integrate structured, unstructured, and graph-based knowledge sources.

Governance and Reliability

• Track token usage, model consumption, API utilization, and operational costs.
• Develop chargeback or showback mechanisms and support AI FinOps reporting and capacity planning.
• Monitor agent execution, tool usage, latency, hallucinations, failure rates, and model quality.
• Implement guardrails, safety controls, prompt protection, data masking, PII protection, and human-in-the-loop validation.

Evaluation and Optimization

• Develop agent and tool evaluation frameworks.
• Apply context engineering, prompt engineering, retrieval optimization, agent tuning, and AI benchmarking.

Mandatory Qualifications

• 8–10 years of software engineering or platform engineering experience.
• At least 3 years of hands-on AI/ML or Generative AI experience.
• Production experience building enterprise-scale AI applications and designing AI architectures and platforms.
• Hands-on Agentic AI experience with Azure, Azure AI Foundry, Azure OpenAI, Python, and LangChain or LangGraph.
• Experience with multi-agent orchestration, RAG, vector databases, AI memory, agent state management, REST APIs, API gateways, event-driven architectures, and messaging systems.
• Experience with AI monitoring, observability, governance, cost or token usage tracking, enterprise data and storage technologies, and SQL.
• Experience designing scalable, secure, and governed AI platforms.

Desirable Skills

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