Sr. Machine Learning Engineer (Hybrid) :: Chicago, IL (Local only)

DMS Vision•Chicago, TX

23h•Hybrid

About The Position

The Sr. Machine Learning Engineer designs, develops, tests, and deploys machine learning, generative AI, and agentic AI solutions in production. The role works with data scientists, software engineers, architects, and DevOps teams to build scalable AI products and platforms, including LLM applications, Retrieval-Augmented Generation (RAG) systems, AI agents, and intelligent automation. The position builds and maintains AI/ML and generative AI pipelines on AWS and Databricks, including automated workflows for data ingestion, feature engineering, training, deployment, prompt optimization, and monitoring. It applies CI/CD, MLOps, and LLMOps practices and develops observability to track performance, drift, hallucinations, latency, cost, and business outcomes. The engineer also manages production systems for reliability, security, scalability, and compliance, while evaluating emerging AI technologies. The role establishes AI governance and responsible AI practices, including documentation, controls, validation, auditability, and consideration of transparency, explainability, fairness, security, privacy, and compliance. It partners with risk, security, legal, and governance stakeholders; provides technical leadership and mentorship; contributes to AI strategy and architecture standards; and identifies opportunities for measurable business value. The position is hybrid and requires the candidate to be local to Chicago, Illinois. The stated duration is 6–12 months.

Requirements

Bachelor's or master's degree in Computer Science, Data Science, Engineering, Artificial Intelligence, or a related field.
At least 7 years of experience in Machine Learning Engineering, AI Engineering, MLOps, Software Engineering, or related disciplines.
At least 3 years of hands-on experience deploying AI/ML solutions in cloud environments.
Demonstrated experience delivering generative AI, LLM, RAG, or agent-based solutions in production.
Strong knowledge of AWS AI/ML services, including SageMaker, Bedrock, Lambda, Step Functions, CloudFormation, ECS/EKS, and related services.
Experience building and deploying machine learning and generative AI applications in production.
Proficiency with LLM frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar agent orchestration frameworks.
Experience designing RAG architectures and integrating vector databases.
Experience implementing AI agents, agentic workflows, and intelligent automation solutions.
Proficiency in Python and related AI/ML libraries and frameworks.
Experience with Docker and Kubernetes or similar containerization and orchestration technologies.
Knowledge of CI/CD, MLOps, LLMOps, model monitoring, and AI observability practices.
Deep understanding of machine learning, deep learning, generative AI, foundation models, and agentic AI architectures.
Strong knowledge of software engineering principles, DevSecOps, MLOps, and LLMOps best practices.
Ability to architect scalable, secure, and resilient AI platforms and intelligent systems.
Ability to analyze complex business problems and apply AI solutions that generate measurable business value.
Strong understanding of responsible AI, governance, explainability, and risk management principles.
Excellent communication skills, including the ability to explain advanced AI concepts to technical and non-technical audiences.
Ability to independently drive AI initiatives from concept through production deployment.
Hands-on technical capability and ability to influence strategy while remaining engaged in solution delivery.
Ability to mentor and develop engineering talent and foster an AI-first culture.
Must be local to Chicago, Illinois.

Responsibilities

Design, develop, test, and deploy machine learning, generative AI, and agentic AI solutions in production.
Collaborate with data scientists, software engineers, architects, and DevOps teams to build scalable AI products and platforms.
Develop and operationalize LLM applications using foundation models and enterprise AI services.
Design and implement RAG architectures using enterprise knowledge repositories, vector databases, and semantic search technologies.
Build and orchestrate AI agents and multi-agent systems for reasoning, planning, workflow execution, and decision support.
Develop prompt engineering frameworks, evaluation methods, and continuous optimization processes.
Build, test, deploy, and maintain AI/ML and generative AI pipelines on AWS and Databricks.
Create automated workflows for data ingestion, preparation, feature engineering, model training and deployment, prompt optimization, and monitoring.
Implement CI/CD, MLOps, and LLMOps practices for AI solution deployment and lifecycle management.
Develop AI observability and monitoring for model performance, drift, hallucinations, latency, cost, and business outcomes.
Manage and optimize production AI systems for reliability, security, scalability, and regulatory compliance.
Evaluate emerging AI technologies, frameworks, and foundation models.
Design agentic workflows integrating AI agents with enterprise systems, APIs, knowledge bases, and business processes.
Develop intelligent automation solutions to streamline operational workflows and improve business efficiency.
Build human-in-the-loop review processes and governance controls for AI-assisted decision-making.
Implement tool-using agents that interact with enterprise applications, databases, and external services while maintaining security and compliance.
Develop and maintain AI/ML documentation, standards, and governance processes.
Ensure AI solutions follow responsible AI principles, including transparency, explainability, fairness, security, privacy, and compliance.
Partner with risk, security, legal, and governance stakeholders on enterprise AI controls and monitoring frameworks.
Support model validation, auditability, and explainability requirements.
Mentor engineers, data scientists, and AI practitioners.
Contribute to AI strategy, architecture standards, and the technology roadmap.
Identify opportunities for AI, generative AI, and intelligent automation to create measurable business value.
Communicate AI concepts, risks, opportunities, and recommendations to technical and business audiences.