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.
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 chargebackshowback 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
Required Qualifications
Experience
7+ years in software engineering or platform engineering.
3+ years building AIML 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 Google Cloud Platform 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
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.
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.
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
HarmRiskContext 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
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