Staff MLOps Engineer

Milwaukee Tool•Menomonee Falls, MT

1d•Hybrid

About The Position

This role can be REMOTE, Onsite or Hybrid INNOVATE without boundaries! At Milwaukee Tool we firmly believe that our People and our Culture are the secrets to our success -- so we give you unlimited access to everything you need to provide support to your business unit. Our people and our culture are the secrets to our success. We empower you to own it, drive it, and do what it takes to support the biggest breakthroughs in the industry. As we expand our machine learning capabilities, we’re looking for a Lead MLOps Engineer to build and scale our ML infrastructure from the ground up with a strong focus on collaborating with domain experts embedded in business units and engineering teams, treating them as internal customers to ensure scalable solutions. Applicants must be authorized to work in the U.S.; Sponsorship is not available for this position. Your Role on Our Team: As an MLOps Engineer at Milwaukee Tool, you will lead the design, deployment, and governance of our machine learning operations. You’ll work closely with and empower ML engineers and data scientists across teams using Databricks, Azure, Citrine and AWS to unify and streamline model development, deployment, and monitoring. This is a hands-on role with broad technical ownership and the opportunity to shape our MLOps strategy and tooling from scratch.

Requirements

5+ years of experience in MLOps, DataOps, DevOps, or backend engineering roles.
Experience with Databricks ML services, SageMaker, Azure ML.
Strong Python skills and familiarity with ML frameworks (e.g., PyTorch, MLflow).
Experience with infrastructure-as-code (Terraform, Spacelift, GitHub Actions, Databricks Asset Bundles, Azure Pipelines) and container orchestration (Docker, Kubernetes).
Proven ability to build CI/CD pipelines and model registries from scratch.
Familiarity with monitoring tools (e.g., Azure Synapse Monitoring, Azure ML Studio monitoring, Databricks Lakehouse Monitoring, CloudWatch, CloudTrail).
Hands-on experience with model and data quality monitoring.
Strong understanding of the ML lifecycle, from data ingestion to model deployment strategies and retraining.
Experience supporting multi-cloud environments and cross-functional collaboration.
Experience maintaining ops pipelines for end user facing solutions, ideally in situations where access to data and/or deployed models may be limited.

Nice To Haves

Experience with GenAI/LLMOps workflows and prompt management.
Knowledge of security and compliance in regulated environments.
Experience deploying ML models to edge devices and working with C/static datatypes in embedded environments.
Familiarity with ML service Citrine, data governance and lineage tools.
Experience with performance testing, observability, and cost optimization for ML workloads.
Familiarity with transformer-based architectures and LLM frameworks (e.g., Hugging Face, OpenAI, LangChain) including prompt orchestration and autonomous agent flows.

Responsibilities

Architect and implement scalable MLOps infrastructure across Databricks, Azure, and AWS.
Build and maintain CI/CD/CT pipelines for model training, validation, deployment, and monitoring.
Collaborate with developers to establish best practices for model versioning, reproducibility, and governance.
Implement observability tools to monitor model performance, drift, latency, and system health.
Collaborate with ML engineers, data scientists, electrical engineers and DevOps to integrate models into production systems.
Work with ML community to define and enforce security, compliance, and privacy standards across the ML lifecycle.
Document and promote MLOps best practices and tooling.
Continuously evolve ML ops pipelines to support the ML pipelines for both internal IT and end user solutions.
Drive awareness and adoption of existing ML tools and platforms across the organization through documentation, training, and internal community engagement.