Role & responsibilities Define and own the AI/ML vision and roadmap aligned with business objectives and the digital strategy of the insurance organization. Lead the design and implementation of ML, deep learning, NLP, and GenAI solutions across core business functions. Spearhead use cases such as: Intelligent claims adjudication and fraud analytics Predictive underwriting and pricing optimization Risk scoring and customer lifetime value modeling Virtual assistants and GenAI for customer engagement OCR/NLP for document automation, NER, Spacey Drive adoption of GenAI technologies (e.g., ChatGPT, LLMs, LangChain) in areas like automated policy servicing, chatbots, and document summarization. Oversee end-to-end ML lifecycle from problem framing, data preparation, model training, to deployment and monitoring. Collaborate with engineering, product, actuarial, and operations teams to embed AI models into digital platforms. Establish robust MLOps practices and ensure scalability, reproducibility, and governance of models. Evaluate and implement AI platforms and tools (e.g. Vertex AI, AWS Sagemaker, Hugging Face, Databricks). Build and lead a team of data scientists, ML engineers, and AI researchers. Stay at the forefront of emerging AI trends and regulatory considerations relevant to the insurance industry. Preferred candidate profile Experience in insurance or fintech domain, with a good understanding of insurance workflows and actuarial concepts. Exposure to AI productivity tools like Cursor.io, GitHub Copilot, or Chat GPT for enhanced development velocity. Strategic thinking combined with the ability to be hands-on when needed. Excellent leadership, stakeholder management, and communication skills. Strong understanding of data privacy, model fairness, explainability, and regulatory compliance (e.g., IRDAI). ------------------------------------------------------------------------------------------------------- Bachelors or Masters degree in Computer Science, Data Science, Engineering, or related field (PhD preferred). 10+ years of experience in data science or machine learning, with at least 35 years in a leadership role. Proven track record of leading AI/ML teams in regulated industries such as insurance, banking, or healthcare. Deep knowledge of machine learning, NLP, deep learning frameworks (Tensor Flow, PyTorch), and model deployment techniques. Experience deploying AI models into production at scale using cloud-native MLOps tools. Expertise in Generative AI, LLMs, Vision LLMs, and Retrieval-Augmented Generation (RAG) is a strong plus. Tech Stack Required- Python , Statistics , Machine Learning Pipelines, Natural Language Processing , LLM, MLOps ,LLMOps, GenAi, AWS, Scikit-lear, keras, Classification | Summarization | Generation | Name Entity Recognition | BIO Tagging | Transformers | CNN | RNN | LSTM | BERT ), Docker | MLflow | Grafana | Wandb | Github Actions / CircleCi / Jenkins | Terraform | Paperspace | Kubernetic |AWS ), LLM, LLMOps, VertexAi, BadRock, OpenAi.
