Senior AI Engineer Agentic AI PlatformSenior AI Engineer Agentic AI Platform

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

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Required Qualifications

Experience

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

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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.

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What Success Looks Like

Within the first 6-12 months, this role will:

Deliver scalable multi-agent AI solutions for enterprise use cases.

Establish reusable AI platform capabilities across multiple business domains.

Implement AI governance, monitoring, and cost attribution frameworks.

Build enterprise-grade orchestration patterns and memory architectures.

Improve AI system reliability, observability, and operational maturity.

Enable business teams to rapidly develop AI-powered applications on a secure, governed platform.

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My assessment based on the transcript

The interviewer was effectively looking for someone who can discuss:

Architecture trade-offs

Agent orchestration patterns

Choreography vs orchestration

Memory management strategies

Graph databases & ontology

AI platform governance

APIM and AI gateway patterns

Closed-loop evaluation

Harm/Risk/Context engineering

Cost attribution and multi-tenant AI platforms

This is why I would title the role as "Senior AI Platform Engineer - Agentic AI " or "Agentic AI Solutions Architect ", even if the requisition is formally called "AI Engineer. " The expectations are clearly architect-level.

Role Descriptions: Senior AI Engineer Agentic AI Platform Essential Skills: Senior AI Engineer Agentic AI Platform Desirable Skills:

Keyword:

Skills: AI Agents

Experience Required: 8-10