AI / ML Engineer - (Modern AI Stack) - Local to MN

AI / ML Engineer - (Modern AI Stack)

Duration: 9 months

Local to MN Candidates

Role Overview

The AI/ML Engineer (Mid Level) will design, build, and deploy modern AI and machine learning solutions for healthcare clients, working closely with consultants, architects, and domain SMEs. This role is focused on applied AI delivery-not research-using cloud-native, GenAI-ready stacks to solve real payer and provider problems.

You will contribute across the lifecycle: data preparation, model development, GenAI integration, deployment, and optimization, with a strong emphasis on responsible AI, scalability, and production readiness.

Key Responsibilities

AI / ML Solution Development

Build and deploy ML and GenAI solutions using modern frameworks and cloud services.

Develop models across use cases such as NLP, predictive analytics, classification, anomaly detection, and decision support.

Implement LLM-based solutions (RAG, prompt engineering, embeddings, agent-assisted workflows) under architectural guidance.

Write clean, maintainable, production-grade code in Python.

Data & Feature Engineering

Perform data ingestion, feature engineering, and data validation across structured and unstructured healthcare data.

Work with large datasets using distributed processing frameworks where applicable.

Partner with data engineers and SMEs to ensure data quality and relevance.

Deployment & MLOps

Package and deploy models using containerized and cloud-native patterns.

Support model monitoring, performance tracking, retraining, and drift detection.

Follow Optum standards for model reviews, documentation, and approvals.

Consulting & Client Delivery

Collaborate with consultants to translate business requirements into technical AI solutions.

Participate in client discussions, solution demos, and working sessions.

Contribute to PoCs, pilots, and scaled implementations.

Responsible AI & Compliance

Ensure solutions meet Responsible AI, security, and compliance requirements.

Implement explainability, traceability, and audit-ready artifacts where required.

Support AI governance and review processes as part of delivery.

Required Qualifications

4-7 years of experience in AI/ML engineering or applied data science roles.

Strong hands-on experience with Python and ML libraries (e.g., scikit-learn, PyTorch, TensorFlow).

Experience building end-to-end ML solutions from data to deployment.

Practical exposure to GenAI / LLM concepts (prompting, embeddings, RAG, APIs).

Experience working in cloud environments (Azure preferred).

Ability to communicate effectively with both technical and non-technical stakeholders.

Preferred Qualifications

Experience in healthcare, insurance, or regulated industries.

Familiarity with Azure ML, Databricks, or similar platforms.

Exposure to agent-based or workflow-oriented AI systems.

Consulting or client-facing delivery experience.

Knowledge of Responsible AI, model governance, or compliance workflows.