Lead Gen AI Engineer - Python/LLM in Hyderabad, भारत

About the Organisation : Our client is an established organisation in the Education Technology and Learning Solutions sector, focused on making quality education and learning opportunities more accessible through https://jobeax.com/link/wSwS22wr80MRWLpC a strong presence in the education ecosystem, the organisation leverages digital platforms and technology-driven solutions to address evolving learning https://jobeax.com/link/PqZTXH8NkHmKwMmD organisation is expanding its AI capabilities and investing in next-generation Generative AI solutions. This opportunity offers the chance to contribute to enterprise-scale AI initiatives, develop intelligent applications and drive the adoption of Agentic AI and LLM-powered https://jobeax.com/link/SFHOY7EhVu5tBl6Q Overview : We are looking for an experienced and hands-on Lead GenAI Engineer to drive the architecture, development and deployment of enterprise-grade Generative AI https://jobeax.com/link/uTkIfl1tAHKRWQPa ideal candidate should have strong expertise in Python, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI and Multi-Agent https://jobeax.com/link/4SD8x5BQOu7zSM5t role requires an engineer who can take AI solutions beyond prototypes and proof-of-concepts and successfully transition them into scalable, reliable and production-ready https://jobeax.com/link/reInf3WklaDLmUVl will be responsible for leading technical implementation, designing AI architectures, optimising model performance, establishing LLMOps/MLOps practices and mentoring engineering https://jobeax.com/link/ASC1tLJdtizIwf1w are looking for someone who combines strong hands-on engineering capabilities with technical leadership and end-to-end solution https://jobeax.com/link/68hTotw3SwSyuY8t Responsibilities : 1. GenAI Architecture & Engineering : - Lead the GenAI roadmap, taking solutions from prototypes and proof-of-concepts to production.- Design, develop and deploy enterprise-grade applications powered by Large Language Models.- Architect and implement Retrieval-Augmented Generation (RAG) pipelines.- Develop LLM-powered applications with contextual retrieval and intelligent response generation.- Design scalable and secure GenAI architectures for enterprise use cases.2. Agentic AI & Multi-Agent Systems : - Design and develop Agentic AI and Multi-Agent systems.- Work with frameworks such as LangGraph and Google Agent Development Kit (ADK).- Implement agent orchestration, tool calling and enterprise system integrations.- Explore and implement MCP (Model Context Protocol) and A2A (Agent-to-Agent) communication.- Build intelligent workflows involving multiple specialised AI agents.3. Edge AI & Model Optimisation : - Drive Edge AI initiatives using technologies such as Ollama and vLLM.- Implement model quantisation, distillation and inference optimisation techniques.- Evaluate efficient model-serving strategies for different deployment environments.- Optimise inference speed, resource utilisation and model performance.4. LLMOps, MLOps & Production Deployment : - Establish LLMOps/MLOps best practices across the GenAI development lifecycle.- Implement CI/CD pipelines for AI application development and deployment.- Establish observability, logging, monitoring, testing and AI guardrails.- Ensure production AI applications meet reliability, scalability and security requirements.- Continuously monitor and improve deployed models and applications.5. Performance, Scalability & Reliability : - Optimise AI systems for scalability, reliability, latency and cost efficiency.- Identify and resolve performance bottlenecks in LLM applications and retrieval pipelines.- Design robust architectures capable of supporting enterprise workloads.- Implement appropriate security controls and production AI best practices.6. Technical Leadership : - Provide technical direction and mentorship to engineering teams.- Conduct architecture reviews, code reviews and technical assessments.- Establish engineering standards and development best practices.- Collaborate with stakeholders to translate business requirements into technical solutions.- Take ownership of end-to-end GenAI engineering https://jobeax.com/link/GTNZKF2kxpVUFhTn Technical Skills : Programming & Development : - Strong hands-on proficiency in Python.- Experience developing APIs and production-grade https://jobeax.com/link/ua3uzM7kwcQ4QKsp AI & LLMs : - Large Language Models (LLMs).- Retrieval-Augmented Generation (RAG).- Agentic AI and Multi-Agent Systems.- Embeddings and Vector Databases.- LLM-powered application https://jobeax.com/link/cVqnBuwSbdbN3RDZ AI Frameworks & Protocols : - LangGraph.- Google Agent Development Kit (ADK).- Model Context Protocol (MCP).- Agent-to-Agent (A2A) https://jobeax.com/link/8D3O6S907pU3jcYr AI & Model Serving : - Ollama.- vLLM.- Model quantisation and distillation.- Inference https://jobeax.com/link/G5Yt900TomIUZAvN / LLMOps : - CI/CD.- Observability and monitoring.- AI guardrails and testing.- Production deployment and lifecycle https://jobeax.com/link/ZMZXGSpVTW0k1zJg Platforms : - AWS.- Google Cloud Platform (GCP).- Microsoft https://jobeax.com/link/At91jXVDl1YAWDJs Experience & Qualifications : - Minimum 8+ years of overall professional experience.- At least 3+ years of hands-on experience building and deploying production GenAI/LLM systems.- Strong practical experience in Python and LLM-based application development.- Demonstrated experience implementing RAG pipelines and Agentic AI solutions.- Hands-on experience with LangGraph and/or Google ADK.- Understanding of embeddings, vector databases and retrieval architectures.- Experience working with at least one major cloud platform : AWS, GCP or Azure.- Practical understanding of LLMOps/MLOps, CI/CD and production AI systems.- Ability to design technical architectures and provide engineering leadership.- Strong analytical, problem-solving and communication https://jobeax.com/link/DkPro09N04CD2GIL We Are Looking For : - The ideal candidate should have personally designed, developed and deployed production-grade GenAI applications.- Be able to explain the architecture, implementation decisions and challenges of systems they have built.- Possess strong knowledge of RAG architecture, agent orchestration and LLM application development.- Understand production challenges involving scalability, security, latency, reliability and cost optimisation.- Be comfortable writing and reviewing code while leading technical initiatives.- Have the ability to mentor engineers and drive engineering https://jobeax.com/link/IxIpSlcdJd0RA7kn : This is a hands-on engineering leadership position, not a purely managerial or product-management role. Candidates with only theoretical AI knowledge, general AI/ML exposure or limited POC experience may not be suitable. (ref:hirist.tech)