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Job Title : Senior AI Platform Engineer - Agentic AI
Location : Chicago, IL
Client: TCS
Rate: $82/hr on W2
Positions: 2
JD :
Job Description
Senior AI Engineer - Agentic AI Platform
Location
Chicago, IL (Hybrid)
3 days onsite (Tuesday to Thursday)
Remote Monday and Friday
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.
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Key Responsibilities
Agentic AI Solution Development
Design and develop sophisticated multi-agent AI systems for enterprise use cases.
Build autonomous and semi-autonomous AI workflows using Agentic AI patterns.
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:
o Rate limiting
o Quota management
o Multi-tenant usage tracking
o Cost attribution
o Authentication & authorization
o Audit logging
Enable structured onboarding and lifecycle management of AI agents.
Multi-Agent Orchestration
Design orchestration frameworks where agents communicate through:
o Direct calls
o Event-driven architectures
o Message queues
o Publish-subscribe patterns
Implement choreography and conductor-based execution models.
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:
o Vector databases
o Semantic caching
o Conversation memory
o Agent state persistence
o 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:
o Token usage
o Model consumption
o API utilization
o 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:
o Agent executions
o Tool usage
o Latency
o Hallucinations
o Failure rates
o Model quality
Create dashboards and operational metrics for enterprise AI workloads.
Responsible AI & Security
Implement:
o Guardrails
o Safety controls
o Prompt protection
o Data masking
o PII protection
o 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:
o Agent evaluation
o Tool evaluation
o Response quality measurement
o Closed-loop evaluation
o Hallucination detection
Apply advanced AI engineering techniques including:
o Context engineering
o Prompt engineering
o Retrieval optimization
o Agent tuning
o AI system benchmarking