{"id":3059633,"url":"https://alion.io/job/javen-technologies-inc-senior-machine-learning-operations-mlops-engineer","title":"Senior Machine Learning Operations (MLOps) Engineer","company":{"id":3806764,"name":"Javen Technologies Inc","domain":"javentechnologies.com","url":"https://alion.io/company/javen-technologies-inc","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cincinnati, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":120000,"max_usd":229000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1268},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Machine Learning","optional":false},{"name":"Airflow","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"Argo Workflows","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Azure DevOps","optional":true},{"name":"Db2","optional":true},{"name":"Feast","optional":true},{"name":"Feature Store","optional":true},{"name":"GCP","optional":true},{"name":"Git","optional":true},{"name":"GitHub","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Incident Management","optional":true},{"name":"Java","optional":true},{"name":"Kubeflow","optional":true},{"name":"Kubernetes","optional":true},{"name":"MLFlow","optional":true},{"name":"Platform Engineering","optional":true},{"name":"Python","optional":true},{"name":"Recommender Systems","optional":true},{"name":"Scala","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-10-20T19:32:03Z","employer_posted_date":null,"last_verified_at":"2026-10-20T19:32:03Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Title: Senior Machine Learning Operations (MLOps) Engineer\nLocation: 38 Fountain Square Plaza, Cincinnati, OH 45202\nDuration: 12 Months contract \nPosition Summary\nWe are seeking a Senior Machine Learning Operations (MLOps) Engineer to design, build, and optimize scalable machine learning platforms and production ML systems. This role will focus on enabling end-to-end machine learning lifecycle management, including feature engineering, model development, deployment automation, monitoring, governance, and continuous improvement.\nThe ideal candidate has strong experience building and operationalizing machine learning solutions in cloud environments, developing CI/CD pipelines for ML workloads, and collaborating with data scientists, data engineers, software engineers, and business stakeholders. Experience working in highly regulated industries such as financial services, banking, healthcare, insurance, or telecommunications is highly desirable.\nKey Responsibilities\nML Platform Engineering & Model Operations\nDesign, develop, deploy, and manage production-grade machine learning solutions.\nBuild scalable and reusable machine learning platforms that support model development, training, validation, deployment, and monitoring.\nImplement best practices for model versioning, experimentation tracking, governance, and reproducibility.\nDevelop frameworks to support both batch and real-time inference workloads.\nOptimize machine learning systems for performance, reliability, maintainability, and cost efficiency.\nMLOps Pipeline Development\nDesign and maintain automated CI/CD/CT (Continuous Integration, Continuous Delivery, Continuous Training) pipelines for machine learning workflows.\nAutomate model testing, validation, deployment, rollback, and retraining processes.\nEstablish infrastructure-as-code practices and automated environment provisioning.\nBuild operational tooling to support model lifecycle management across development, testing, and production environments.\nFeature Store & Data Engineering Integration\nDesign and support enterprise feature stores to improve feature reuse, consistency, and model governance.\nCollaborate with data engineering teams to integrate machine learning solutions with data warehouses, data lakes, and operational data sources.\nDevelop scalable data pipelines supporting both training and inference workloads.\nOptimize data availability, quality, lineage, and observability.\nProduction Monitoring & Reliability\nImplement model monitoring frameworks covering:\nModel performance\nData quality\nData drift\nConcept drift\nFeature drift\nSystem health and reliability\nEstablish alerting, observability, and incident management processes.\nDevelop strategies for model retraining and continuous performance improvement.\nReal-Time Machine Learning Systems\nBuild and support low-latency scoring and prediction platforms.\nDevelop event-driven machine learning architectures using streaming technologies.\nPartner with business and engineering teams to enable near real-time decisioning capabilities.\nGovernance, Security & Compliance\nImplement MLOps governance standards and controls.\nEnsure compliance with enterprise security, privacy, risk, and regulatory requirements.\nSupport model auditability, explainability, lineage tracking, and documentation.\nPartner with risk, compliance, and cybersecurity teams to maintain operational standards.\nCollaboration & Technical Leadership\nCollaborate closely with data scientists, analytics teams, product owners, and software engineers.\nTranslate business requirements into scalable technical solutions.\nMentor junior engineers and contribute to MLOps standards, patterns, and best practices.\nDrive adoption of modern machine learning engineering methodologies across the organization.\nRequired Qualifications\nExperience\n5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.\n3+ years of experience building and supporting production machine learning systems.\nDemonstrated experience implementing end-to-end machine learning lifecycle management.\nExperience operating ML workloads in cloud environments (AWS, Azure, or Google Cloud Platform).\nExperience supporting both batch and real-time machine learning solutions.\nExperience working with large-scale structured and unstructured data.\nRequired Technical Skills\nMachine Learning & MLOps\nML lifecycle management\nModel deployment and orchestration\nExperiment tracking and model versioning\nModel monitoring and observability\nFeature Store architecture\nModel Registry implementation\nAutomated retraining workflows\nResponsible AI and model governance practices\nCloud Platforms\nExperience with one or more:\nAWS SageMaker\nAzure Machine Learning\nGoogle Vertex AI\nProgramming\nProficiency in:\nPython (required)\nSQL (required)\nJava, Scala, or R (preferred)\nData Platforms\nExperience with:\nSnowflake\nDatabricks\nIBM DB2 or equivalent enterprise RDBMS platforms\nData lakes and modern cloud storage platforms\nData Transformation\nExperience with:\ndbt\nSpark\nPandas\nPySpark\nDevOps & Infrastructure\nExperience with:\nGit/GitHub/GitLab\nJenkins, GitHub Actions, Azure DevOps, or equivalent CI/CD tools\nTerraform or infrastructure-as-code frameworks\nLinux administration\nShell scripting\nContainers & Orchestration\nExperience with:\nDocker\nKubernetes\nOpenShift (preferred)\nStreaming & Real-Time Technologies\nExperience with:\nApache Kafka\nEvent-driven architectures\nReal-time inference pipelines\nMonitoring & Observability\nExperience with:\nPrometheus\nGrafana\nELK Stack\nCloud-native monitoring solutions\n Preferred Qualifications\nExperience implementing enterprise Feature Stores.\nExperience designing Model Registry and ML Governance frameworks.\nExperience building real-time decisioning or scoring platforms.\nExperience supporting credit risk, fraud, customer analytics, recommendation systems, or predictive analytics use cases.\nExperience with MLFlow, Kubeflow, Airflow, Argo Workflows, Feast, SageMaker Pipelines, or similar MLOps platforms.\nKnowledge of software architecture patterns and distributed systems.\nFamiliarity with AI/ML regulatory frameworks and model risk management practices.\nRelevant cloud certifications (AWS, Azure, Google Cloud Platform) preferred.\n Desired Outcomes\nThe engineer will help establish and mature enterprise MLOps capabilities, including:\nFeature Store implementation and adoption.\nModel Registry and governance processes.\nAutomated CI/CD/CT pipelines.\nReal-time model deployment and scoring.\nEnterprise machine learning monitoring and observability.\nReusable machine learning platform services that accelerate model development and deployment across data science 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