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.
Benefits
- 401(k)
- Dental insurance
- Health insurance
- Paid time off
- Vision insurance
Tags & focus areas
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