Responsibilities

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.

Basic qualifications

Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related quantitative field.
Experience: 8+ years in data science, machine learning, or AI solution delivery, with 3+ years in a technical leadership or lead role.
Strong proficiency in Python and major ML/DL frameworks — TensorFlow, PyTorch, Scikit-learn, XGBoost, etc.
Solid understanding of ML algorithms, neural networks, and natural language processing (NLP) concepts.
Hands-on experience with cloud-based ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI).
Exposure to Generative AI, LLMs, LangChain, OpenAI, or agentic AI frameworks.
Familiarity with data pipelines, feature stores, and model serving using MLOps tools (MLflow, Kubeflow, or Airflow).
Strong communication, collaboration, and stakeholder engagement skills.
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).
Lead a skilled technical team and influence enterprise-level data and AI strategy.
Collaborate in a fast-paced, forward-thinking environment with opportunities for extension and growth.

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