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Specialized MLOps/Databricks skillset with metro locations and mid-senior experience yields moderate candidate competition.
Technical MLOps skills transfer across industries, but claims/healthcare preference increases domain sensitivity.
Explicit 6–9 years and mandatory Databricks, MLflow, LangChain, Azure expertise create stringent shortlisting filters.
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Own and automate end-to-end ML lifecycle management on Databricks including environment setup, model deployment, CI/CD pipeline creation, and job scheduling for AI/ML workloads.
Develop frameworks and utilities to ensure reproducible and scalable ML experimentation and deployment, including LLM/GenAI integration such as embedding models and LangChain agentic workflows.
Collaborate closely with Data Scientists and cross-functional teams to translate experimental notebooks into production pipelines and maintain platform controls for environment consistency and model versioning.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
6–9 years of experience in ML Engineering, MLOps, or platform engineering.
Strong hands-on experience with Databricks, Spark (batch/streaming), Python, Scala; CI/CD tools (Git, GitHub Actions, Jenkins, Azure DevOps); MLflow for tracking and model packaging.
Experience deploying AI/ML models into cloud environments (preferably Azure) and working with embedding models, semantic vectors, LLM components, and LangChain.
Experienced in building and managing scalable ML pipelines on Databricks within complex domains, preferably in healthcare claims or Payment Integrity.
Proficient in integrating advanced LLM/GenAI solutions (embedding models, RAG architectures, LangChain) into ML workflows.
Strong operational focus on automation, reproducibility, environment and dependency management, and cross-team collaboration to enable robust ML delivery.