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Milwaukee Tool · 2 days ago

Staff MLOps Engineer

Menomonee Falls, WI

Full-time

Hybrid

Senior Level, Lead/Staff

5+ years exp

Milwaukee Tool is a company that values innovation and a strong culture, and they are seeking a Staff MLOps Engineer to lead the design, deployment, and governance of their machine learning operations. The role involves building and scaling ML infrastructure while collaborating with cross-functional teams to ensure scalable solutions and best practices in MLOps.

Consumer GoodsElectronicsIndustrialManufacturingSoftware

Growth Opportunities

No H1B

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

Qualification

MLOpsDatabricksAzureAWSPythonCI/CD pipelinesInfrastructure-as-codeContainer orchestrationMonitoring toolsML lifecycleGenAI/LLMOpsSecurity compliancePerformance testingTransformer architectures

Required

Applicants must be authorized to work in the U.S.; Sponsorship is not available for this position

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

Preferred

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