Principal Data Scientist (GenAI) · Turing Turing · Varies · Remote (India, EST overlap) · 1w ago Varies Remote (India, EST overlap) 1w ago

Principal Data Scientist (GenAI) | Pay not stated | Remote (India, with 4-hour EST overlap)

Turing is hiring a Principal GenAI Engineer to lead enterprise-scale AI implementations for Fortune 500 clients. This role focuses on building Graph-powered RAG systems (Graph-RAG) that combine structured semantic reasoning with LLM architectures to deliver scalable, explainable, production-grade AI solutions. 1 position available, with an immediate start within 1 week.

What You’ll Do

• Develop and optimize LLM-based solutions: Lead the design and deployment of large language models using prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.
• Own the codebase: Build, maintain, and review high-quality Python code (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.
• Deploy to the cloud: Help deploy GenAI applications on AWS, Azure, or GCP, optimizing resource usage and ensuring robust CI/CD processes.
• Collaborate across teams: Work with product owners, data scientists, and business SMEs to define requirements and deliver AI products.
• Mentor: Provide technical leadership and knowledge-sharing to the engineering team.

Requirements

• 10+ years of experience in ML/AI systems with a strong Data Science background
• 2+ years of hands-on experience with LLMs (RAG, agents, prompt engineering)
• Strong proficiency in Python, LangGraph, and SQL
• Experience deploying GenAI systems on AWS, Azure, or GCP
• Immediate availability (within 1 week)

Good to Have: Knowledge Graph Expertise

• Design and scale enterprise Knowledge Graph architectures
• Develop ontologies, taxonomies, and semantic data models
• Implement entity resolution, relationship extraction, and graph enrichment
• Experience with Neo4j, Amazon Neptune, or similar graph databases
• Hands-on experience with Cypher or similar graph query languages
• Build hybrid retrieval systems combining Knowledge Graphs and vector databases
• Integrate structured graph reasoning with LLMs to reduce hallucination and improve explainability

Location & Schedule

Remote position based in India, requiring a 4-hour overlap with EST working hours.