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Mid-level ML role in Bangalore with broad LLM/MLOps skills and a reputable employer increases applicant competition.
Core ML, LLM, and MLOps skills are transferable, though insurance domain experience is beneficial.
Explicit 3–6 years plus required Databricks, LLM, MLOps, and specific tech stack makes filters strict.
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Develop and deploy end-to-end machine learning solutions for various insurance use cases including claims triage, fraud detection, underwriting risk scoring, and reserve estimation.
Build and operationalize large language model (LLM) powered applications such as claim summarization and intelligent document extraction, including fine-tuning and prompt engineering foundation models (e.g., GPT-4, Gemini).
Implement and maintain scalable data pipelines and ML lifecycle management including CI/CD, model monitoring, automated retraining, and version control using tools like Databricks, MLflow, and Git.
3–6 years hands-on experience in data science, machine learning, or AI engineering.
Advanced proficiency in Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow) and strong SQL skills including complex queries and optimization.
Experience with Databricks or similar platforms (Delta Lake, Spark, MLflow, Workflows).
Proven experience building, deploying, and maintaining LLM and RAG-based applications, including prompt engineering and use of vector databases.
Experienced in delivering scalable ML solutions in the insurance domain, particularly for claims and underwriting processes.
Skilled in managing the full ML lifecycle end-to-end, including data engineering, model deployment, and automated monitoring in production environments.
Comfortable working with cutting-edge AI technologies such as foundation models, agentic AI frameworks, and modern MLOps tools, demonstrating operational excellence and ownership.