{"id":1714687,"url":"https://alion.io/job/entarian-mlops-engineer","title":"MLOps Engineer","company":{"id":2163029,"name":"Entarian","domain":"entarian.com","url":"https://alion.io/company/entarian","size_band":"501-1000","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":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Arlington, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":119000,"max_usd":225000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1129},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Datadog","optional":false},{"name":"Evidently AI","optional":false},{"name":"GCP","optional":false},{"name":"GDPR","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Grafana","optional":false},{"name":"JavaScript","optional":false},{"name":"Jenkins","optional":false},{"name":"Kibana","optional":false},{"name":"Kubeflow","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NIST AI RMF","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"SQL","optional":false},{"name":"AIOps","optional":true},{"name":"Docker","optional":true},{"name":"Helicone","optional":true},{"name":"HIPAA","optional":true},{"name":"InfluxDB","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangSmith","optional":true},{"name":"LLM","optional":true},{"name":"Matplotlib","optional":true},{"name":"Plotly","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Seaborn","optional":true},{"name":"Time Series Forecasting","optional":true},{"name":"TimescaleDB","optional":true}],"status":"live","first_seen_at":"2026-10-02T18:22:22Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-11T20:40:10Z","board_verified":true,"closed_at":null,"days_open":9,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":9},"description":"Overview/ Job Responsibilities\nJob Summary\nWe are seeking a skilled MLOps Engineer to join our team and ensure the seamless deployment, monitoring, and optimization of AI models in production.\nThe MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI-related logging. This role will involve building scalable infrastructure and dashboards for real-time and historical insights, ensuring models are secure, performant, and aligned with business needs.\nKey Responsibilities\nModel Deployment: Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS SageMaker, ensuring scalability and low latency.\n Monitoring and Observability: Build and maintain dashboards using Grafana, Prometheus, or Kibana to track real-time model health (e.g., accuracy, latency) and historical trends.\nData Drift Detection: Implement drift detection pipelines using tools like Evidently AI or Alibi Detect to identify shifts in data distributions and trigger alerts or retraining.\nLogging and Tracing: Set up centralized logging with ELK Stack or OpenTelemetry to capture AI inference events, errors, and audit trails for debugging and compliance.\nPipeline Automation: Develop CI/CD pipelines with GitHub Actions or Jenkins to automate model updates, testing, and deployment.\nSecurity and Compliance: Apply secure-by-design principles to protect data pipelines and models, using encryption, access controls, and compliance with regulations like GDPR or NIST AI RMF.\nCollaboration: Work with data scientists, AI Integration Engineers, and DevOps teams to align model performance with business requirements and infrastructure capabilities.\n Optimization: Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource usage on cloud platforms like AWS, Azure, or Google Cloud.\nDocumentation: Maintain clear documentation of pipelines, dashboards, and monitoring processes for cross-team transparency. \nMinimum Qualifications\nQualifications\nEducation: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.\n Experience:\n5+ years in MLOps, DevOps, or software engineering with a focus on AI/ML systems.\nProven experience deploying models in production using MLflow, Kubeflow, or cloud platforms (AWS SageMaker, Azure ML).\nHands-on experience with observability tools like Prometheus, Grafana, or Datadog for real-time monitoring.\n Technical Skills:\nProficiency in Python and SQL; familiarity with JavaScript or Go is a plus.\nExpertise in containerization (Docker, Kubernetes) and CI/CD tools (GitHub Actions, Jenkins).\nKnowledge of time-series databases (e.g., InfluxDB, TimescaleDB) and logging frameworks (e.g., ELK Stack, OpenTelemetry).\nExperience with drift detection tools (e.g., Evidently AI, Alibi Detect) and visualization libraries (e.g., Plotly, Seaborn).\n AI-Specific Skills:\nUnderstanding of model performance metrics (e.g., precision, recall, AUC) and drift detection methods (e.g., KS test, PSI).\nFamiliarity with AI vulnerabilities (e.g., data poisoning, adversarial attacks) and mitigation tools like Adversarial Robustness Toolbox (ART).\n Soft Skills:\nStrong problem-solving and debugging skills for resolving pipeline and monitoring issues.\nExcellent collaboration and communication skills to work with cross-functional teams.\nAttention to detail for ensuring accurate and secure dashboard reporting.\nMust be eligible to obtain a Department of Homeland Security EOD clearance ( Requirements 1. US Citizenship, 2. Favorable Background Investigation) \nDesired Qualifications\nPreferred Qualifications\nExperience with LLM monitoring tools like LangSmith or Helicone for generative AI applications.\nKnowledge of compliance frameworks (e.g., GDPR, HIPAA) for secure data handling.\nContributions to open-source MLOps projects or familiarity with X platform discussions on #MLOps or #AIOps.\nAbout Us\nFormed through the strategic union of Sev1Tech and ERT, Entarian is a premier provider of mission-critical engineering and technology solutions. Founded on a legacy of excellence dating back to 1993, Entarian is a product of an evolved and fully diversified engineering and federal technology leader. From deep space to defense and civilian missions, Entarian delivers secure, mission-aligned digital solutions that drive national resilience and operational effectiveness. We don't just support modernization; we define it.\nJoin the Mission and Start your Career Journey: Apply Directly via our Careers Portal Connect, Referrals & Inquiries? Email the team: \nEntarian is an Equal Opportunity and Affirmative Action Employer. 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