MLOps Engineer 9Core ML + MLOps

It's fun to work in a company where people truly BELIEVE in what they are doing! We're committed to bringing passion and customer focus to the business. Job DescriptionEL3 – Databricks MLOps Engineer (Contract) Domain: Claims Payment Integrity | M&R, C&S, E&I Claims (preferred) Actuarial & Forecasting Analytics Exposure is an Added Advantage Tech Stack: Databricks, Spark, Python, Scala, Azure, GitHub Actions, Terraform AI/LLM Capabilities: Embedding Models, LLM Integration, LangChain Agentic Frameworks Role Summary The EL3 Databricks MLOps Engineer is a senior hands-on role responsible for enabling end-to-end machine learning lifecycle automation on Databricks. This includes building and maintaining the CI/CD infrastructure, environment configuration, packaging and deploying ML models, supporting reproducible experiments, and ensuring scalable job orchestration for AI/ML workloads, including LLM-based applications. The role partners closely with Data Scientists, AI/ML Engineers, platform teams, and business stakeholders within Claims Payment Integrity to ensure robust, reliable, and automated ML delivery. Key Responsibilities Enable and automate the end-to-end ML lifecycle on Databricks (environment setup, model workflow automation, job scheduling, monitoring hooks). Build frameworks, templates, and utilities that make ML development and experimentation reproducible and scalable. Implement CI/CD pipelines using Git, GitHub Actions, Jenkins, Azure DevOps, or similar tools. Package, version, and deploy ML models into Databricks-managed execution environments. Set up automated workflows for training, retraining, evaluation, and scheduled job execution. Support creation and integration of machine learning models including classification, forecasting, anomaly detection, NLP, and PI models. Enable LLM/GenAI-driven solutions by integrating:  Embedding model generation RAG architectures Vector databases LangChain agentic workflows Optimize resource usage, runtime configurations, and code execution patterns for ML workloads. Collaborate with Data Scientists to translate experimental notebooks into production-ready pipelines. Implement platform-level controls for environment consistency, dependency management, access control, and model versioning. Support troubleshooting, debugging, and performance improvements for ML workloads. Document standards, templates, guidelines, and best practices for MLOps teams. Work cross-functionally with product, engineering, and analytics teams across PI. Required Qualifications Bachelor’s/Master’s degree in Computer Science, Engineering, or related field 6–9 years of relevant experience in ML Engineering, MLOps, or platform engineering Strong hands-on experience with Databricks, Spark (batch/streaming), Python, Scala Experience enabling ML lifecycle tools such as MLflow (tracking, packaging, model registration) Strong CI/CD experience using Git, GitHub Actions, Jenkins, or Azure DevOps