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
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.
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:
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.
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
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
Call Center datasets (member & provider interactions)
Provider RCM datasets (billing, coding, authorizations)
EHR/clinical datasets for cross-domain validation
