Sr. GenAI Engineer (FS/BE)

About this role

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. Senior GenAI Engineer – Backend / Fullstack Location: Remote Employment Type: Full Time Experience Level: Senior (7–9 years) About the Role Turing is hiring a Senior GenAI Engineer to design, build, and deploy enterprise-grade Generative AI solutions for Fortune 500 clients. This role sits at the intersection of backend/fullstack engineering and applied AI , with a strong focus on developing scalable, production-grade GenAI applications powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agent-based architectures. You will work closely with product, engineering, and data teams to integrate GenAI capabilities into real-world enterprise applications while ensuring reliability, scalability, security, and performance in production environments. What We’re Looking For 7–9 years of professional software engineering experience , primarily in backend or fullstack development 2+ years of hands-on experience with Generative AI and LLM-based applications , including RAG, AI agents, and prompt engineering Strong experience designing and developing production-grade backend and distributed systems Strong proficiency in Python Hands-on experience building and consuming REST APIs, microservices, and distributed services Strong experience with SQL and NoSQL databases Practical experience with LangChain, LangGraph, LlamaIndex, or similar GenAI frameworks Experience designing and implementing RAG pipelines , including document ingestion, chunking, embeddings, retrieval, and response generation Experience working with commercial or open-source LLMs and APIs Hands-on experience deploying applications on AWS, Azure, or GCP Strong understanding of system design, scalability, performance optimization, and production reliability Key Responsibilities Design, develop, and deploy scalable GenAI applications using LLMs, RAG, and agentic architectures Build robust backend services, APIs, and microservices for AI-powered applications Develop and optimize RAG pipelines , including data ingestion, embedding generation, retrieval, reranking, and context management Build AI agents and multi-step workflows using orchestration frameworks such as LangGraph or equivalent technologies Integrate LLM capabilities into enterprise applications and existing technology ecosystems Evaluate and optimize LLM applications for accuracy, latency, cost, scalability, and reliability Implement LLM evaluation, observability, monitoring, guardrails, and responsible AI practices Troubleshoot and resolve performance and production issues across GenAI applications Collaborate with product managers, data scientists, ML engineers, and software engineers to deliver end-to-end AI solutions Participate in architecture discussions, code reviews, and technical design decisions Follow software engineering best practices for testing, documentation, security, and CI/CD Good to Have Experience with vector databases such as Pinecone, Weaviate, Milvus, Qdrant, Chroma, or FAISS Experience with semantic search, hybrid search, reranking, and embedding models Familiarity with frontend frameworks such as React or Next.js for fullstack development Experience with Docker, Kubernetes, CI/CD pipelines, and DevOps practices Understanding of LLMOps, model evaluation, prompt evaluation, fine-tuning, and model serving Experience with LLM observability/evaluation platforms or frameworks Familiarity with open-source and commercial LLM ecosystems Experience building multi-tenant, high-scale, or enterprise SaaS platforms Understanding of AI security, data privacy, prompt injection mitigation, and GenAI guardrails Why Join Turing Work on cutting-edge Generative AI solutions for leading global enterprises Build and deploy real-world AI systems at production scale Work across modern LLM, RAG, and agentic AI architectures Collaborate with highly skilled engineering, AI, and product teams Take strong ownership of technical implementation and contribute to key architectural decisions Solve challenging engineering problems at the intersection of GenAI and large-scale software systems