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Steampunk

Federal software solutions delivering user-focused outcomes

MLOps Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeMcLean, VA, USA$115k - $150k /yrIn-person

About the Job

Requirements

  • Ability to hold a position of public trust with the U.S. government.
  • A bachelor's or master's degree in Computer Science, Data Engineering, Machine Learning, Information Systems, or a related technical discipline, or no degree with six years of experience.
  • At least two years of experience in MLOps, ML engineering, DevOps, cloud engineering, or applied ML development.
  • Proficiency in Python and familiarity with ML frameworks such as scikit-learn, TensorFlow, PyTorch, or XGBoost.
  • Hands-on experience with at least one cloud platform (AWS, Azure, or GCP) and associated ML/DevOps services such as SageMaker, Azure ML, Vertex AI, EKS, AKS, or GKE.
  • Practical experience with CI/CD tools such as GitHub Actions, GitLab CI, or Jenkins, and containerization using Docker or Kubernetes.
  • Strong understanding of ML lifecycle management, including versioning, packaging, deployment, monitoring, and retraining.
  • Familiarity with infrastructure-as-code tools such as Terraform or CloudFormation.
  • Experience with logging, observability, and monitoring frameworks such as CloudWatch, Prometheus, Grafana, ELK stack, or Datadog.
  • Ability to collaborate with Data Scientists, Engineers, and mission stakeholders to ensure ML systems deliver operational value.
  • Ability to document workflows, architecture decisions, and runbooks.

Responsibilities

  • Develop and maintain end-to-end ML pipelines, including data ingestion, feature engineering, model training, model packaging, deployment, and monitoring workflows.
  • Implement CI/CD pipelines for ML assets to enable automated testing, versioning, promotion, and reproducibility across environments.
  • Integrate ML models into production services using APIs, microservices, serverless functions, or container orchestration frameworks such as Kubernetes.
  • Build and manage ML platform components such as model registries, experiment tracking systems, feature stores, datasets, job schedulers, and lineage tools.
  • Monitor model performance, system health, and data drift using logging, observability frameworks, dashboards, and alerting systems, and partner with Data Scientists to refine retraining strategies.
  • Collaborate with Data Engineers to ensure data pipelines and data quality support high-performing ML systems.
  • Implement DevSecOps practices, including secrets management, environment hardening, and secure deployment patterns, to ensure compliance and operational resilience.
  • Help define and enforce MLOps standards, documentation, and reusable patterns that improve efficiency and reduce technical debt across teams.
  • Troubleshoot and perform root-cause analysis of pipeline issues, infrastructure problems, or performance degradation in deployed ML models.
  • Stay current with emerging MLOps tools, cloud-native ML technologies, distributed training methodologies, and best practices in ML lifecycle management.
  • Contribute to the growth of the AI & Data Exploitation Practice.

Desired Qualifications

  • AWS ML Specialty certification.
  • AWS DevOps Engineer certification.
  • Azure Data Scientist Associate certification.
  • Google Professional Machine Learning Engineer certification.
  • Databricks Machine Learning Associate or Professional certification.

About the Company

Steampunk

Federal software solutions delivering user-focused outcomes

Steampunk helps U.S. federal government agencies deliver digital programs and services. The company partners with government clients, using a startup-style, customer-centered delivery approach to design, develop, and deploy software and services that support mission goals. Its products and work are executed in close collaboration with clients, emphasizing rapid delivery and high-quality user experiences. Steampunk differentiates itself by focusing specifically on federal customers and by embedding client input at every step of the process, from planning through deployment, so outcomes meet user needs and mission requirements at speed. The overall goal is to produce reliable, usable solutions that achieve clear mission impacts for government programs.

Company size
501-1,000
Company stage
Not listed
Total funding
Not listed
Headquarters
Winston-Salem, NC
Founded
2002

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