MLOps Engineer

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 automationon 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 Integrityto ensure robust reliable and automated ML delivery.

• Enable and automate the end-to-end ML lifecycleon 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 modelsincluding 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.

• Bachelors/Masters degree in Computer Science Engineering or related field

• 69 yearsof 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 LangChainfor 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 datasetsfor cross-domain validation