{"id":113118,"url":"https://alion.io/job/nextgen-healthcare-ml-operations-engineer","title":"ML Operations Engineer","company":{"id":3086,"name":"NextGen Healthcare","domain":"nextgen.com","url":"https://alion.io/company/nextgen-healthcare","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":84,"open_postings":4,"ghost_share":0,"stale_share":0.5,"repost_share":0.25,"time_to_fill_p50_days":54,"computed_at":"2026-10-21T05:46:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":89000,"max_usd":202000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":47},"experience_years_min":2,"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":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"GitLab CI","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2025-03-24T00:00:00Z","employer_posted_date":"2025-03-24","last_verified_at":"2026-10-21T06:24:21Z","board_verified":true,"closed_at":null,"days_open":576,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":576},"description":"Job Description:\nThe Machine Learning Operations (MLOps) Engineer will support our AI/ML initiatives by streamlining the deployment, monitoring, and scaling of machine learning models in production environments. The incumbent will have a solid understanding of machine learning workflows, DevOps principles, and cloud technologies, with a focus on optimizing machine learning pipelines and ensuring reliable and efficient operations.Model Deployment and Integration:\nImplement and maintain CI/CD pipelines for deploying machine learning models to production environments.\n\nEnsure seamless integration of machine learning models into existing software systems.\n\nInfrastructure and Automation:\nDesign and manage scalable infrastructure for training, testing, and serving machine learning models.\n\nAutomate data preprocessing, model training, and deployment workflows.\n\nMonitoring and Optimization:\nMonitor the performance of deployed models and systems, identifying and resolving issues proactively.\n\nOptimize model inference latency, scalability, and resource utilization.\n\nCollaboration:\nWork closely with data scientists, software engineers, and product teams to understand requirements and deliver operational solutions.\n\nCollaborate with DevOps and cloud engineering teams to ensure infrastructure reliability and security.\n\nData and Model Management:\nMaintain version control for datasets, models, and code.\n\nImplement best practices for data and model governance, ensuring compliance with organizational and regulatory requirements.\n\nContinuous Improvement:\nStay updated with the latest trends in MLOps tools, frameworks, and practices.\n\nRecommend and implement improvements to the MLOps processes and infrastructure.\n\nPerform other duties that support the overall objective of the position.\nEducation Required:\nBachelor’s degree in Computer Science, Data Science, Engineering, or a related field.\n\nOr, any combination of education and experience which would provide the required qualifications for the position.\n\nExperience Required:\n2-3 years of hands-on experience in MLOps, DevOps, or related roles.\nExperience with MLOps tools and platforms like MLflow, Kubeflow, or SageMaker.\nExperience with feature stores and model versioning systems.\nExperience in building CI/CD pipelines using tools like Jenkins, GitLab CI, or similar.\nKnowledge, Skills & Abilities:\nKnowledge of: Proficiency in Python and familiarity wStrong understanding of containerization and orchestration tools (e.g., Docker,\nKubernetes). Strong understanding of containerization and orchestration tools (e.g., Docker, Kubernetes). Familiarity with distributed computing frameworks (e.g., Apache Spark). Knowledge of cloud platforms such as AWS, Azure, or Google Cloud. Solid understanding of model monitoring, logging, and debugging tools. Familiarity with database technologies and data pipelines (SQL, NoSQL, ETL/ELT processes).\nSkill in: Strong problem-solving skills and a detail-oriented mindset. Excellent communication and collaboration abilities.\nAbility to: \nThe company has reviewed this job description to ensure that essential functions and basic duties have been included. It is intended to provide guidelines for job expectations and the employee's ability to perform the position described. It is not intended to be construed as an exhaustive list of all functions, responsibilities, skills and abilities. Additional functions and requirements may be assigned by supervisors as deemed appropriate. This document does not represent a contract of employment, and the company reserves the right to change this job description and/or assign tasks for the employee to perform, as the company may deem appropriate.\nNextGen Healthcare is an equal opportunity employer. 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