MLOps Engineer

Steampunk; Bloomington (IL); On-site; USD 115,000 - 150,000; Full time

Job summary

Steampunk is seeking a seasoned MLOps Engineer to design, build, and operate scalable ML infrastructure across client engagements in Bloomington, IL. You will operationalize ML models, implement robust pipelines, and ensure smooth transitions from experimentation to production.

You will collaborate with Data Scientists, Data Engineers, and cloud teams to deploy, monitor, and manage ML lifecycle components, with a focus on security, reliability, and maintainability in a government-facing

Qualifications

• Develop and maintain end-to-end ML pipelines including data ingestion, feature engineering, model training, 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 like Kubernetes.
• Build and manage core ML platform components such as model registries, experiment tracking, feature stores, datasets, job schedulers, and lineage tools.
• Monitor model performance, system health, and data drift using logging, observability frameworks, dashboards, and alerting systems; 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 best 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.
• Support troubleshooting and 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.
• You will contribute to the growth of our AI & Data Exploitation Practice!

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, enabling automated testing, versioning, promotion, and reproducibility across environments.
• Integrate ML models into production services using APIs, microservices, serverless functions, or container orchestration frameworks like Kubernetes.
• Build and manage core 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; partner with Data Scientists to refine retraining strategies.
• Collaborate with Data Engineers to ensure data pipelines and data quality support high-performing ML systems.
• ImplementDevSecOpsbest practices—includingsecretsmanagement, environment hardening, and secure deployment patterns—to ensure compliance and operational resilience.
• Help define and enforceMLOpsstandards, documentation, and reusable patterns that improve efficiency and reduce technical debt across teams.
• Support troubleshooting and root-cause analysis of pipeline issues, infrastructure problems, or performance degradation in deployed ML models.
• Stay current with emergingMLOpstools, cloud-native ML technologies, distributed training methodologies, and best practices in ML lifecycle management.
• You will contribute to the growth of our AI & Data Exploitation Practice!

Skills

Python

ML pipelines

Kubernetes

Docker

CI/CD

Terraform

Cloud platforms (AWS/Azure/GCP)

Observability

GitHub Actions

Collaboration with Data Scientists

Education

Bachelor's or Master's degree in Computer Science or related technical discipline

Masters Degree and 0 years of experience OR Bachelors Degree and 2 years of experience OR No degree and 6 years of experience

Tools

GitHub Actions

GitLab CI

Jenkins

Docker

Job description

Overview

We are seeking aMLOpsEngineerto design, build, and support the infrastructure, tooling, and automation that enable scalable and reliable machine learning systems across our client engagements. This roleis responsible foroperationalizing ML models, implementing robust pipelines, and ensuring smooth transitions from experimentation to production. TheMLOpsEngineer works closely with Data Scientists, AI Developers, Data Engineers, and cloud engineering teams to streamline model deployment, monitoring, and lifecycle management in alignment with mission needs.

Contributions

• Develop andmaintainend-to-end ML pipelines, including data ingestion, feature engineering, model training, model packaging, deployment, and monitoring workflows.

• Implement CI/CD pipelines for ML assets, enabling automated testing, versioning, promotion, and reproducibility across environments.

• Integrate ML models into production services using APIs, microservices, serverless functions, or container orchestration frameworks like Kubernetes.

• Build and manage core 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; partner with Data Scientists to refine retraining strategies.

• Collaborate with Data Engineers to ensure data pipelines and data quality support high-performing ML systems.

• ImplementDevSecOpsbest practices—includingsecretsmanagement, environment hardening, and secure deployment patterns—to ensure compliance and operational resilience.

• Help define and enforceMLOpsstandards, documentation, and reusable patterns that improve efficiency and reduce technical debt across teams.

• Support troubleshooting and root-cause analysis of pipeline issues, infrastructure problems, or performance degradation in deployed ML models.

• Stay current with emergingMLOpstools, cloud-native ML technologies, distributed training methodologies, and best practices in ML lifecycle management.

• You will contribute to the growth of our AI & Data Exploitation Practice!

Qualifications

• Ability to hold a position of public trust with the U.S. government.

• Bachelors or Master’s degree in Computer Science, Data Engineering, Machine Learning, Information Systems, ora relatedtechnical discipline.


• Masters Degree and 0 years of experience OR Bachelors Degree and 2 years of experience OR No degree and 6 years of experience.

• 2+ years of experience inMLOps, ML engineering, DevOps, cloud engineering, or applied ML development.

• Proficiencyin Python and familiarity with ML frameworks such as scikit-learn, TensorFlow,PyTorch, orXGBoost.

• Hands-on experience with at least one cloud platform (AWS, Azure, or GCP) and associated ML/DevOps services (e.g., SageMaker, Azure ML, Vertex AI, EKS/AKS/GKE).

• Practical experience with CI/CD tools (GitHub Actions, GitLab CI, Jenkins) and containerization (Docker, 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 (CloudWatch, Prometheus, Grafana, ELK stack, Datadog, etc.).

• Ability to collaborate with Data Scientists, Engineers, and mission stakeholders to ensure ML systems deliver operational value.

• Strong communicationskills and the ability to document workflows, architecture decisions, and runbooks.

• Preferred certifications:
  
  
  • AWS ML Specialty
  
  
  • AWS DevOps Engineer
  
  
  • Azure Data Scientist Associate
  
  
  • Google Professional Machine Learning Engineer
  
  
  • Databricks Machine Learning Associate/Professional
  
  
  
  

About steampunk

Steampunk relies on several factors to determine salary, including but not limited to geographic location, contractual requirements, education, knowledge, skills, competencies, and experience. The projected compensation range for this position is $115,000 to $150,000. The estimate displayed represents a typical annual salary range for this position. Annual salary is just one aspect of Steampunk’s total compensation package for employees. Learn more about additional Steampunk benefits here.

Identity Statement

As part of the application process, you are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.

Steampunk is a Change Agent in the Federal contracting industry, bringing new thinking to clients in the Homeland, Federal Civilian, Health and DoD sectors. Through our Human-Centered delivery methodology, we are fundamentally changing the expectations our Federal clients have for true shared accountability in solving their toughest mission challenges. As an employee owned company, we focus on investing in our employees to enable them to do the greatest work of their careers – and rewarding them for outstanding contributions to our growth. If you want to learn more about our story, visit http://www.steampunk.com.