Sr. GenAI Engineer (FS/BE)

No. of positions: 1

Remote/India, EST overlap 4 hours

Full Stack/Backend development experience

Immediate- 1week availability

About the Role

Turing is hiring a Senior 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+ years of experience in ML/AI systems with strong Software development background (Full Stack or Backend)
• 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.