Incedo - Generative AI Lead - LLM/RAG in Gurgaon, भारत

Job Description :As a Lead - Generative AI & NLP at Incedo, you will provide technical leadership in architecting and delivering enterprise-scale AI solutions powered by LLMs, NLP, and advanced information-retrieval https://jobeax.com/link/HFo45m3uYoWyCnoW will own the technical direction for GenAI initiatives, define solution architecture and engineering standards, and lead teams in building secure, scalable, observable, and production-ready AI https://jobeax.com/link/uTkIfl1tAHKRWQPa role requires a combination of deep hands-on technical expertise, architecture capability, delivery ownership, and team https://jobeax.com/link/UElKnrRfFoXd0J4r Responsibilities:- Own the technical architecture and delivery of enterprise GenAI/NLP platforms and applications.- Define solution strategies for complex use cases involving RAG, conversational AI, document intelligence, knowledge assistants, information extraction, summarization, and enterprise search.- Design scalable end-to-end architectures covering data ingestion, document processing, NLP pipelines, embeddings, vector/hybrid search, LLM orchestration, APIs, security, monitoring, and deployment.- Evaluate and recommend LLMs, embedding models, vector databases, retrieval approaches, orchestration frameworks, and deployment architectures based on business requirements.- Establish enterprise patterns for RAG architecture, prompt engineering, LLM evaluation, model selection, grounding, guardrails, and responsible AI.- Lead the development of reusable GenAI components, frameworks, accelerators, and reference architectures.- Drive optimization of AI solutions across quality, latency, scalability, reliability, security, and infrastructure/LLM cost.- Define evaluation strategies and benchmarks for LLM and RAG applications, including retrieval quality, factuality, relevance, hallucination, and business-task accuracy.- Provide technical direction on frameworks such as Hugging Face, LangChain, LlamaIndex, and other emerging GenAI technologies.- Guide architecture and implementation of vector-search platforms using FAISS, Pinecone, Weaviate, Milvus, or equivalent technologies.- Lead the integration of AI solutions with enterprise APIs, data platforms, applications, and cloud infrastructure.- Establish engineering practices around API design, testing, CI/CD, observability, security, MLOps, and production readiness.- Mentor senior developers and engineers, conduct design/code reviews, and build technical capability within the team.- Work directly with architects, engineering leaders, product stakeholders, and clients to understand business problems and shape AI solutions.- Lead technical discussions, solution presentations, POCs, estimations, and architecture reviews.- Identify emerging GenAI technologies and assess their applicability to enterprise use cases.- Take ownership of technical risks, architectural decisions, and resolution of complex production issues.- Drive multiple workstreams and ensure delivery against quality, timeline, and business https://jobeax.com/link/NuhNRw5p6aI2YmsI Skills :- Expert-level Python development skills with strong knowledge of software architecture, APIs, concurrency, performance, and scalable application design.- Strong hands-on experience architecting and delivering LLM/GenAI and NLP solutions in production.- Deep expertise in RAG, LLM orchestration, embeddings, semantic/hybrid search, information retrieval, prompt engineering, and LLM evaluation.- Strong understanding of NLP pipelines, document processing, information extraction, classification, summarization, and conversational AI.- Extensive experience with Hugging Face, LangChain, LlamaIndex, or comparable LLM/NLP ecosystems.- Strong knowledge of vector databases such as FAISS, Pinecone, Weaviate, and Milvus.- Experience designing Python-based services and AI platforms using FastAPI/Flask and microservices architectures.- Strong understanding of cloud-native application development and production engineering.- Experience with Docker, Kubernetes, CI/CD, MLOps, monitoring, and observability.- Strong understanding of AI system security, data privacy, access control, and responsible AI considerations.- Excellent problem-solving, technical communication, stakeholder management, and leadership skills. (ref:hirist.tech)