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 Description EL3 – 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 - Experience deploying AI/ML models into cloud environments (Azure preferred) - Ability to create and integrate embedding models , semantic vectors, and LLM-driven components - Experience with LangChain  for agentic workflows and integration of tools/functions - Strong problem-solving, debugging, and collaboration skills Preferred Qualifications - Experience with Azure OpenAI or OpenAI-compatible LLM APIs - Familiarity with healthcare claims workflows, PI, FWA, provider billing, or pricing - Experience in Agile/Scrum environments - Strong understanding of software engineering best practices, packaging, dependency management Good-to-Have Data Knowledge - Call Center datasets  (member & provider interactions) - Provider RCM datasets  (billing, coding, authorizations) - EHR/clinical datasets  for cross-domain validation If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us! Not the right fit?  Let us know you're interested in a future opportunity by clicking  Introduce Yourself  in the top-right corner of the page or create an account to set up email alerts as new job postings become available that meet your interest!