Senior AI Engineer Agentic AI Platform

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