{"id":1650885,"url":"https://alion.io/job/steampunk-mlops-engineer","title":"MLOps Engineer","company":{"id":3854754,"name":"Steampunk","domain":"steampunk.com","url":"https://alion.io/company/steampunk-5","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"iCIMS","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["McLean, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":115000,"max":150000,"currency":"USD","period":"year","gross":null,"usd_annual":150000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Databricks","optional":false},{"name":"Datadog","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"Google GKE","optional":false},{"name":"Grafana","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-10-02T00:14:38Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-06T01:18:42Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Overview\nWe are seeking a MLOps Engineer to design, build, and support the infrastructure, tooling, and automation that enable scalable and reliable machine learning systems across our client engagements. This role is responsible for operationalizing ML models, implementing robust pipelines, and ensuring smooth transitions from experimentation to production. The MLOps Engineer 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.\nContributions\nDevelop and maintain end-to-end ML pipelines, including data ingestion, feature engineering, model training, model packaging, deployment, and monitoring workflows. \nImplement CI/CD pipelines for ML assets, enabling automated testing, versioning, promotion, and reproducibility across environments. \nIntegrate ML models into production services using APIs, microservices, serverless functions, or container orchestration frameworks like Kubernetes. \nBuild and manage core ML platform components such as model registries, experiment tracking systems, feature stores, datasets, job schedulers, and lineage tools. \nMonitor model performance, system health, and data drift using logging, observability frameworks, dashboards, and alerting systems; partner with Data Scientists to refine retraining strategies. \nCollaborate with Data Engineers to ensure data pipelines and data quality support high-performing ML systems. \nImplement DevSecOps best practices-including secrets management, environment hardening, and secure deployment patterns-to ensure compliance and operational resilience. \nHelp define and enforce MLOps standards, documentation, and reusable patterns that improve efficiency and reduce technical debt across teams. \nSupport troubleshooting and root-cause analysis of pipeline issues, infrastructure problems, or performance degradation in deployed ML models. \nStay current with emerging MLOps tools, cloud-native ML technologies, distributed training methodologies, and best practices in ML lifecycle management. \nYou will contribute to the growth of our AI & Data Exploitation Practice! \nQualifications\nAbility to hold a position of public trust with the U.S. government. \nBachelors or Master’s degree in Computer Science, Data Engineering, Machine Learning, Information Systems, or a related technical discipline. \nMasters Degree and 0 years of experience OR Bachelors Degree and 2 years of experience OR No degree and 6 years of experience. \n2+ years of experience in MLOps, ML engineering, DevOps, cloud engineering, or applied ML development. \nProficiency in Python and familiarity with ML frameworks such as scikit-learn, TensorFlow, PyTorch, or XGBoost. \nHands-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). \nPractical experience with CI/CD tools (GitHub Actions, GitLab CI, Jenkins) and containerization (Docker, Kubernetes). \nStrong understanding of ML lifecycle management, including versioning, packaging, deployment, monitoring, and retraining. \nFamiliarity with infrastructure-as-code tools such as Terraform or CloudFormation. \nExperience with logging, observability, and monitoring frameworks (CloudWatch, Prometheus, Grafana, ELK stack, Datadog, etc.). \nAbility to collaborate with Data Scientists, Engineers, and mission stakeholders to ensure ML systems deliver operational value. \nStrong communication skills and the ability to document workflows, architecture decisions, and runbooks. \nPreferred certifications: \nAWS ML Specialty \nAWS DevOps Engineer \nAzure Data Scientist Associate \nGoogle Professional Machine Learning Engineer \nDatabricks Machine Learning Associate/Professional \nAbout steampunk\nSteampunk 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.\nIdentity Statement\nAs 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.\nSteampunk 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. If you want to learn more about our story, visit http://www.steampunk.com.\nWe are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. 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