MLOps Engineer
Core
Senior MLOps Engineer enabling end-to-end machine learning lifecycle automation on Databricks for AI/ML workloads and LLM-based applications.
Role type
Senior hands-on IC MLOps Engineer
Builds
CI/CD pipelines, reproducible ML frameworks, automated training/retraining workflows, and production ML model deployments on Databricks.
Domain
Insurance claims payment integrity, actuarial analytics, and healthcare claims (M&R, C&S, E&I)
Deliverable
production ML models
Required skills
Databricks, Apache Spark (batch/streaming), Python, Scala, MLflow, CI/CD (GitHub Actions/Jenkins/Azure DevOps), cloud deployment (Azure), embedding models, RAG architectures, LangChain agentic workflows, vector databases, resource optimization, model versioning, troubleshooting ML workloads
Preferred skills
Azure OpenAI/OpenAI-compatible LLM APIs, healthcare claims workflows (PI, FWA, provider billing), Agile/Scrum, software engineering best practices
Technologies
Databricks, Spark, Python, Scala, Azure, GitHub Actions, Terraform, MLflow, LangChain, Vector databases
Responsibilities
Automate end-to-end ML lifecycle (environment setup, workflow automation, job scheduling, monitoring); Build frameworks/templates for reproducible ML development; Implement CI/CD pipelines; Package and deploy ML models into Databricks environments; Set up automated workflows for training, retraining, and evaluation; Integrate LLM/GenAI solutions (embedding models, RAG, LangChain); Optimize resource usage and runtime configurations; Collaborate with Data Scientists to translate notebooks to production pipelines; Implement platform controls for environment consistency and access; Troubleshoot and debug ML workloads; Document MLOps standards and best practices
Seniority
Senior, hands-on IC
