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Niche Databricks, MLflow and LLM skillset plus seniority reduces competition despite Mumbai metro.
Core MLOps skills are transferable, but healthcare claims domain preference moderately increases sensitivity.
Explicit 6–9 years plus mandatory Databricks, Spark, MLflow, Azure and LLM tooling creates high filter strictness.
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Own end-to-end ML lifecycle automation on Databricks including environment setup, CI/CD pipelines, model packaging, deployment, and job orchestration for AI/ML workloads.
Develop and maintain frameworks, templates, and utilities to ensure reproducible and scalable ML experimentation and production pipelines.
Collaborate closely with Data Scientists, AI/ML Engineers, and business stakeholders to enable robust, automated ML delivery and integrate LLM/GenAI-powered solutions including embedding models and LangChain workflows.
6–9 years of experience in ML Engineering, MLOps, or platform engineering.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
Hands-on experience with Databricks, Spark (batch/streaming), Python, Scala, MLflow, and CI/CD tools like GitHub Actions, Jenkins, or Azure DevOps.
Experience deploying AI/ML models in cloud environments (Azure preferred) and integrating embedding models, semantic vectors, and LLM-driven components.
Experienced in managing complex ML lifecycle automation in a Databricks environment supporting AI/ML production workloads including LLM applications.
Strong technical operator who can translate data science experiments into production-grade pipelines and optimize ML workloads for scalability and reliability.
Domain knowledge or interest in claims payment integrity, forecasting analytics, and working cross-functionally with platform, product, and analytics teams.