Engineering Manager-AI & ML(Lead)

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.