Git MLOps Engineer

” Design and implement advanced analytics solutions for healthcare payers “ Analyze intricate challenges and develop actionable insights “ Mentor and guide junior team members in their professional growth “ Maintain exceptional standards in deliverables “ Build and nurture substantial relationships with clients “ Navigate complex project environments and adapt to changing needs “ Collaborate with healthcare experts and data scientists to drive innovation “ Contribute to the development of impactful solutions in a consulting setting, Role Descriptions: Role Summary:The MLOps Engineer is responsible for building| automating| deploying| monitoring| and maintaining Machine Learning (ML) models and platforms in production environments. The role bridges Data Science| Software Engineering| and DevOps practices to ensure scalable| reliable| and secure ML solutions.Key ResponsibilitiesDesign| build| and maintain end-to-end ML pipelines for training| testing| deployment| and monitoring.Automate CI/CD processes for machine learning models and data workflows.Deploy and manage ML models on cloud platforms such as Azure| AWS| or GCP.Implement model monitoring| performance tracking| drift detection| and retraining strategies.Collaborate with Data Scientists| Data Engineers| and Application Development teams to operationalize ML solutions.Manage containerization and orchestration using Docker and Kubernetes.Ensure security| compliance| scalability| and reliability of ML platforms.Optimize infrastructure utilization and cloud costs for

” Master’s Degree in Computer Science, Data Engineering, Software Engineering preferred “ -At least 4 years of experience in software engineering or data engineering “ Experience operating debt at scale on Databricks “ Prior work with healthcare analytics packages “ Designing CI/CD pipelines for Databricks Asset Bundles “ Provisioning Azure Databricks workspaces and managing isolation patterns “ Building Python CLI tools with argparse and YAML configuration “ Designing AI agent systems for development workflows “ Familiarity with corporate network constraints “ Experience with multi-cloud data flows, ML workloads.Troubleshoot production issues and perform root cause analysis.Maintain documentation| standards| and best practices for MLOps processes.Required SkillsStrong programming skills in Python.Experience with ML frameworks such as TensorFlow| PyTorch| or Scikit-learn.Hands-on experience with CI/CD tools (Azure DevOps| Jenkins| GitHub Actions).Knowledge of Docker| Kubernetes| and Infrastructure as Code (Terraform| ARM| etc.).Experience with cloud platforms (Azure ML| AWS SageMaker| GCP Vertex AI).Familiarity with version control systems such as Git.Knowledge of monitoring and logging tools (Prometheus| Grafana| ELK| Azure Monitor).Understanding of data engineering concepts and ETL pipelines.Preferred QualificationsBachelors or Masters degree in Computer Science| Data Science| Engineering| or related field.Relevant certifications in Azure| AWS| Kubernetes| or DevOps.Experience implementing enterprise-scale AI/ML solutions.Key CompetenciesProblem-solving and analytical thinkingAutomation-first mindsetCollaboration and stakeholder managementContinuous learning and innovationStrong communication skills Essential Skills: Role Summary:The MLOps Engineer is responsible for building| automating| deploying| monitoring| and maintaining Machine Learning (ML) models and platforms in production environments. The role bridges Data Science| Software Engineering| and DevOps practices to ensure scalable| reliable| and secure ML solutions.Key ResponsibilitiesDesign| build| and maintain end-to-end ML pipelines for training| testing| deployment| and monitoring.Automate CI/CD processes for machine learning models and data workflows.Deploy and manage ML models on cloud platforms such as Azure| AWS| or GCP.Implement model monitoring| performance tracking| drift detection| and retraining strategies.Collaborate with Data Scientists| Data Engineers| and Application Development teams to operationalize ML solutions.Manage containerization and orchestration using Docker and Kubernetes.Ensure security| compliance| scalability| and reliability of ML platforms.Optimize infrastructure utilization and cloud costs for ML workloads.Troubleshoot production issues and perform root cause analysis.Maintain documentation| standards| and best practices for MLOps processes.Required SkillsStrong programming skills in Python.Experience with ML frameworks such as TensorFlow| PyTorch| or Scikit-learn.Hands-on experience with CI/CD tools (Azure DevOps| Jenkins| GitHub Actions).Knowledge of Docker| Kubernetes| and Infrastructure as Code (Terraform| ARM| etc.).Experience with cloud platforms (Azure ML| AWS SageMaker| GCP Vertex AI).Familiarity with version control systems such as Git.Knowledge of monitoring and logging tools (Prometheus| Grafana| ELK| Azure Monitor).Understanding of data engineering concepts and ETL pipelines.Preferred QualificationsBachelors or Masters degree in Computer Science| Data Science| Engineering| or related field.