Role overview
- We are seeking a Lead Machine Learning Engineer to spearhead the design, development, and deployment of AI/ML-driven solutions that enhance automation, insights, and decision-making across insurance operations.
- This role combines hands-on machine learning expertise, leadership responsibilities, and exposure to cutting-edge GenAI and LLM-based frameworks.
- The successful candidate will lead a small, high-performing team to research, prototype, and operationalize AI capabilities for real-world business use cases in underwriting, claims, and customer engagement.
What you'll work on
- Research & Innovation: Explore and implement state-of-the-art AI/ML and GenAI techniques, adapting them to business challenges within the insurance domain.
- Model Development: Build, train, and deploy classical ML and deep learning (DL) models using structured and unstructured data.
- Domain Understanding: Rapidly grasp new business domains and data ecosystems to design contextually relevant ML solutions.
- GenAI & LLM Integration: Experiment with and apply Large Language Models (LLMs), agentic AI frameworks, and generative models for automation, NLP, and decision support use cases.
- Team Leadership: Guide and mentor a small team of data scientists and ML engineers; oversee project delivery from conception to production.
- MLOps & Scalability: Implement best practices for MLOps, version control, model governance, monitoring, and retraining pipelines.
- Stakeholder Collaboration: Partner with business and data engineering teams to ensure AI solutions are aligned with strategic objectives and scalable across the enterprise.
What we're looking for
- Background in insurance, finance, or regulated industries with data-heavy processes.
- Experience integrating AI solutions into enterprise data warehouses or customer-facing applications.
- Knowledge of data privacy, AI ethics, and responsible AI practices.
- Working familiarity with Databricks, Snowflake, or Vector Databases (Pinecone, FAISS).
Tags & focus areas
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