Principal GenAI Engineer
Location: Gurugram - 3 Days WFO
Employment Type: Full Time
Experience Level: Principal (8–14 years)
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
- 8-13 years of experience in ML/AI systems
- 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.