MLOps Engineer: Build Production ML Pipelines

Steampunk

Bloomington (IL)

On-site

USD 115,000 - 150,000

Full time

14 days+

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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!