Data Scientist / Engineer (AI)

TL;DR

Data Scientist / Engineer (AI): Building and productionizing data pipelines and LLM-based agents for the insurance industry with an accent on RAG, agentic frameworks, and multi-modal data. Focus on designing autonomous subrogation solutions, implementing Chain-of-Thought prompting, and establishing rigorous evaluation frameworks.

Location: Hybrid in Boston, US

Salary: $100,000 - $110,000 USD

Company

AI-driven SaaS platform transforming the insurance industry through accurate, explainable, and secure insurance-grade AI agents.

What you will do

• Build and productionize data pipelines optimized for LLMs and multi-modal models (text, documents, images).
• Design and deploy LLM-based solutions utilizing RAG, embeddings, and instruction tuning for claims handling and document understanding.
• Experiment with Agentic AI technologies such as LangChain, LangGraph, and OpenAI Agent SDK to develop autonomous subrogation solutions.
• Develop Chain-of-Thought and ReAct prompting strategies to ensure legal compliance across different jurisdictions.
• Establish rigorous LLM-as-a-judge evaluation frameworks to ensure unbiased and explainable decisions.
• Lead client workshops, present prototypes, and help define the product roadmap priorities.

Requirements

• Expert proficiency in production-level object-oriented programming (OOP).
• Proven experience with LLMs and GenAI techniques, including RAG, embeddings, and prompt engineering.
• Solid foundation in ML fundamentals and experience with the full ML lifecycle (evaluation, monitoring, and deployment).
• Experience implementing robust data pipelines for OCR and multi-modal data workflows.
• Proficiency with agentic frameworks (LangChain, LangGraph, CrewAI, etc.) integrated with Databricks or Azure.
• Ability to translate complex business needs into actionable technical solutions and manage stakeholder expectations.

Nice to have

• Deep expertise in Databricks Ecosystem, including Mosaic AI, Unity Catalog, and Delta Lake.
• Strong understanding of Spark data architecture.
• Experience using MLflow for experiment tracking and prompt engineering in the AI Playground.

Culture & Benefits

• Flexible remote and hybrid working options.
• Competitive base salary with a variable component tied to personal and company performance.
• Focus Fridays: a half-day each month dedicated to personal growth and learning.
• Generous PTO and paid holidays.
• Mental health benefits and paid volunteering days (MAD Days).