Ai Machine Learning Engineer Jobs in Chicago, IL

JOB SUMMARY
We are seeking a highly experienced Sr Machine Learning Engineer to design, develop, deploy, and scale enterprise-grade Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions. The ideal candidate will possess deep expertise in AI platform engineering, cloud-native architectures, MLOps, and intelligent automation. This role requires hands-on experience building production-ready AI applications, Retrieval-Augmented Generation (RAG) solutions, AI agents, and large-scale machine learning platforms while driving AI innovation, governance, and business transformation across the organization.
KEY RESPONSIBILITIES
AI/ML Engineering & Solution Development
• Design, develop, test, deploy, and maintain Machine Learning, Generative AI, and Agentic AI solutions in production environments.
• Collaborate with Data Scientists, Software Engineers, Architects, and DevOps teams to deliver scalable AI products and enterprise platforms.
• Build and operationalize Large Language Model (LLM) applications using foundation models and enterprise AI services.
• Design and implement Retrieval-Augmented Generation (RAG) architectures integrating enterprise knowledge repositories, vector databases, and semantic search capabilities.
• Develop AI-powered applications utilizing advanced prompt engineering, context management, and reasoning techniques.
• Build and orchestrate AI agents and multi-agent systems capable of autonomous reasoning, planning, workflow execution, and decision support.
• Establish prompt engineering frameworks, evaluation methodologies, and optimization processes to improve AI application performance and reliability.
• Translate business requirements into scalable AI-driven solutions that deliver measurable business value.
AI Platform Engineering & MLOps
• Design, build, deploy, and maintain AI/ML and Generative AI platforms on AWS and Databricks.
• Develop automated pipelines for:
- Data Ingestion
- Data Preparation
- Feature Engineering
- Model Training
- Model Deployment
- Prompt Optimization
- Model Monitoring
• Implement and maintain MLOps and LLMOps frameworks for enterprise-scale AI lifecycle management.
• Develop CI/CD automation processes supporting AI application delivery and model deployment.
• Build AI observability and monitoring solutions to track:
- Model Performance
- Data Drift
- Hallucinations
- Latency
- Cost Optimization
- Business Outcomes
• Ensure production AI systems meet requirements for reliability, scalability, performance, security, and compliance.
• Evaluate emerging AI technologies, frameworks, platforms, and foundation models for enterprise adoption.
AGENTIC AI & INTELLIGENT AUTOMATION
• Design and implement agentic AI workflows integrated with enterprise systems, APIs, databases, and knowledge repositories.
• Develop intelligent automation solutions that increase operational efficiency and reduce manual effort.
• Build human-in-the-loop review mechanisms and governance workflows for AI-assisted decision making.
• Develop tool-using AI agents capable of securely interacting with enterprise applications, APIs, and external services.
• Implement agent orchestration patterns to support complex business workflows and decision automation.
AI GOVERNANCE & RESPONSIBLE AI
• Develop and maintain documentation, standards, policies, and governance frameworks for AI and Machine Learning solutions.
• Ensure compliance with Responsible AI principles, including:
- Transparency
- Explainability
- Fairness
- Privacy
- Security
- Regulatory Compliance
• Partner with Risk, Security, Legal, and Governance teams to establish enterprise AI controls and monitoring capabilities.
• Support model validation, explainability, auditability, and compliance requirements.
• Implement governance controls for AI lifecycle management and operational oversight.
LEADERSHIP & STRATEGY
• Serve as a technical leader and mentor to AI Engineers, Data Scientists, and Software Engineering teams.
• Contribute to enterprise AI strategy, architecture standards, and technology roadmaps.
• Identify opportunities to leverage AI, Generative AI, and Intelligent Automation to create business value.
• Communicate complex AI concepts, risks, and recommendations to both technical and non-technical stakeholders.
• Promote AI best practices, engineering excellence, and continuous innovation across the organization.
• Drive adoption of emerging AI technologies and modern engineering methodologies.
REQUIRED QUALIFICATIONS
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• Minimum 8 years of experience in:
- AI Engineering
- Machine Learning Engineering
- MLOps
- Software Engineering
- Related Technical Disciplines
• Minimum 3 years of hands-on experience deploying AI/ML solutions in cloud environments.
• Proven experience delivering production-ready:
- Generative AI Solutions
- Large Language Model (LLM) Applications
- Retrieval-Augmented Generation (RAG) Systems
- Agent-Based Solutions
• Strong hands-on experience with AWS AI and cloud services, including:
- Amazon SageMaker
- Amazon Bedrock
- AWS Lambda
- AWS Step Functions
- AWS CloudFormation
- Amazon ECS
- Amazon EKS
• Strong experience building and deploying AI applications in production environments.
• Expertise with AI development frameworks and orchestration platforms, including:
- LangChain
- LangGraph
- LlamaIndex
- Semantic Kernel
- CrewAI
- AutoGen
• Experience designing and implementing RAG architectures and vector database solutions.
• Experience building AI agents, multi-agent systems, and intelligent automation workflows.
• Advanced Python programming skills and experience with AI/ML libraries and frameworks.
• Experience with:
- Docker
- Kubernetes
- Containerized Deployments
- Cloud-Native Architectures
• Strong experience implementing:
- CI/CD Pipelines
- MLOps Frameworks
- LLMOps Platforms
- Model Monitoring Solutions
- AI Observability Practices
• Strong understanding of Software Engineering and DevSecOps best practices.
• Experience architecting scalable, resilient, and secure AI platforms.
PREFERRED QUALIFICATIONS
• Experience with Databricks-based AI and Machine Learning platforms.
• Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, or similar technologies.
• Familiarity with enterprise knowledge management and semantic search platforms.
• Experience implementing advanced AI governance and Responsible AI frameworks.
• Experience building enterprise intelligent automation and decision intelligence solutions.
• Knowledge of model evaluation frameworks and AI benchmarking methodologies.
• Experience working in regulated industries requiring strict governance and compliance standards.
• Experience supporting enterprise AI transformation initiatives.
CERTIFICATIONS
• AWS Certified Machine Learning - Specialty (Preferred)
• AWS Certified Solutions Architect - Associate or Professional (Preferred)
• Databricks Certified Machine Learning Professional (Preferred)
• Kubernetes Certifications (CKA / CKAD) (Preferred)
• Generative AI, MLOps, or AI Engineering Certifications (Preferred)
• Cloud Architecture Certifications (Preferred)