Data Scientist

Data Scientist – GenAI Expert

AI-Powered Product Engineering Team

Role Overview

We're looking for a Data Scientist with deep GenAI expertise to help design and optimize LLM-powered pipelines for AI-driven products. This role sits at the intersection of prompt engineering and production Gen AI and ML systems.

Key Responsibilities

• Design, build, and optimize retrieval-augmented generation (RAG) architectures and LLM orchestration pipelines
• Own prompt engineering and fine-tuning efforts to improve output quality, tone, and consistency
• Benchmark and evaluate multiple LLM providers for quality, cost, and fit for purpose
• Build evaluation frameworks for content quality, factual accuracy, and compliance adherence where relevant
• Diagnose and resolve issues in LangGraph based AI pipelines, including prompt structure, context retrieval, and output consistency
• Work with engineering to productionize models and pipelines, including vector databases and retrieval systems
• Partner with stakeholders to translate business and quality requirements into technical pipeline logic
• Support workflow orchestration with AI-driven quality checks and human-in-the-loop review steps

Required Skills & Experience

• 6-10 years of experience in data science/applied ML, with at least 1-2 years hands-on with LLMs/GenAI in production
• Strong Python skills; experience with LangChain, LangGraph, or equivalent orchestration frameworks
• Hands-on experience with RAG architectures, vector databases (e.g., PgVector, Weaviate, FAISS etc), and embedding models
• Experience with major LLM APIs (OpenAI/Azure OpenAI, Anthropic, Google Vertex AI, or other providers)
• Strong prompt engineering skills, including structured prompting and few-shot design
• Solid understanding of NLP evaluation metrics and methods for assessing generative output quality

Good to Have

• Experience in regulated or compliance-sensitive domains
• Multilingual content experience or fluency-tuning for non-English outputs
• Experience with content or product platforms integrated into broader marketing/tech stacks
• Familiarity with model comparison/benchmarking methodologies across multiple LLM providers