MLOps Engineer

Job Description We are seeking a hands-on MLOps Engineer to support the deployment and operationalization of machine learning solutions within a research‑driven, highly technical environment. This role sits at the intersection of software engineering, machine learning, and cloud infrastructure, with a strong focus on building scalable, secure, and production‑ready ML systems. This position supports advanced analytics and AI initiatives across discovery, development, and operational teams, helping translate machine learning models into reliable, real‑world applications.

• Own and manage the ML deployment pipeline end-to-end
• Design and implement scalable, cost-effective deployment strategies
• Deploy and support ML models using: Docker; PyTorch; XGBoost / scikit-learn
• Ensure security of proprietary data and IP, including access controls
• Automate testing, enforce code quality, and establish deployment best practices
• Collaborate with data scientists and engineers to productionize ML solutions
• Identify infrastructure gaps and propose continuous improvements
• Educate team members on deployment and engineering standards We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day.

We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.

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https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements

• Experience in MLOps or a similar role, with proven success deploying machine learning models to production
• Experience designing, building, and managing end‑to‑end MLOps pipelines
• Strong experience with cloud computing, particularly AWS
• Experience building and managing API endpoints for ML services
• Experience with MLOps tools and orchestration frameworks (e.g., MLflow, Kubeflow, Airflow, or equivalent solutions)
• Familiarity with ML frameworks and libraries such as pandas, NumPy, scikit‑learn, and PyTorch
• Experience with Infrastructure as Code tools (e.g., CloudFormation, Terraform)
• Strong problem‑solving skills with the ability to work both independently and collaboratively
• Ability to clearly articulate the impact of prior work, including: Why specific models, tools, or deployment strategies were chosen; The outcomes or improvements those decisions enabled
• Kubernetes or container orchestration experience AWS ECR, Fargate, AWS Batch
• Built end-to-end MLOps pipelines for deep learning models
• Experience in research-driven or scientific computing environments