Senior AI Engineer / Senior AI Platform Engineer - Agentic AI

Job Title : Senior AI Platform Engineer - Agentic AI

Location : Chicago, IL

Client: TCS

Rate: $82/hr on W2

Positions: 2

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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