Principal Data Scientist with AI

Employer description and requirements

No. of positions: 1

Remote/India, EST overlap 4 hours

Strong DataScience background must

Immediate- 1week availability

About the Role

Turing is hiring a Principal GenAI Engineer with strong expertise in LLMs 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 advanced LLM architectures to deliver scalable, explainable, production-grade AI solutions.

What We’re Looking For

10+ years of experience in ML/AI systems with strong Data Science background

2+ years hands-on experience with LLMs (RAG, agents, prompt engineering)

Strong proficiency in Python, LangGraph, and SQL

Experience deploying GenAI systems on AWS / Azure / GCP

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

Strong hands-on experience with Cypher (or similar graph query languages)

Build hybrid retrieval systems combining Knowledge Graphs + vector databases

Integrate structured graph reasoning with LLMs to reduce hallucination and improve explainability

Roles & Responsibilities

Develop and optimize LLM-based solutions: Lead the design and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.

Codebase ownership: Build and maintain/review high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.

Cloud integration: Aide in deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.

Cross-functional collaboration: Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.

Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.