Q
Senior AI Engineer - Agentic AI Platform
Q1 Technologies
3 hours ago
Full-time
On-site
Chicago, Illinois, United States
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 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
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
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 • 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
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 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
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
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 • 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