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.

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