Senior ML Platform Engineer — Production-Scale AI Infra

Datavant; Lansing (MI); On-site; USD 168,000 - 280,000; Full time

Job summary

Datavant is seeking a Staff ML Platform Engineer to lead the ML Platform team. You will set direction for training, serving, and observability, and own the paved-road framework enabling data scientists to move from config to production efficiently.

You will influence vendor choices and drive secure, HIPAA-conscious data handling. You will mentor engineers, shape architecture for LLM-serving across multiple providers, and stay hands-on with code and infrastructure as code to deliver robust data

Qualifications

• 10+ years of software engineering experience, with 3+ years designing and operating enterprise-scale ML platforms in production.
• Strong technical judgment under ambiguity and a track record of setting standards across teams.
• Hands-on production experience with Databricks and/or SageMaker, MLflow, and at least one core ML framework (PyTorch/TF).

Responsibilities

• Set technical direction across ML training, serving, and observability, and be the escalation point for infrastructure problems.
• Own and evolve the paved-road framework for data science to production workflows.
• Lead architecture for LLM-endpoint serving across managed providers and self-hosted deployments.
• Own standards and tooling for MLflow, model registry, and observability across training and inference.
• Partner with teammates to present a cohesive ML platform to Data Science, App Dev, and Operations teams at Datavant.
• Contribute to vendor and platform selection decisions for model providers and tooling.
• Mentor senior engineers and contribute to infrastructure-as-code alongside teammates.

Skills

Python

Java

Spark

AWS

PyTorch

LLM deployment

Databricks

Terraform

Kubernetes

MLflow

Tools

Databricks

SageMaker

MLflow

Terraform

Kubernetes

Iceberg

Delta

Unity Catalog

Job description

Datavant is seeking a Staff ML Platform Engineer to lead the ML Platform team. You will set direction for training, serving, and observability, and own the paved-road framework enabling data scientists to move from config to production efficiently.

You will influence vendor choices and drive secure, HIPAA-conscious data handling. You will mentor engineers, shape architecture for LLM-serving across multiple providers, and stay hands-on with code and infrastructure as code to deliver robust data