{"id":2003579,"url":"https://alion.io/job/incedo-generative-ai-lead","title":"Generative AI Lead","company":{"id":177799,"name":"Incedo","domain":"incedoinc.com","url":"https://alion.io/company/incedo","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, 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Learning","optional":false},{"name":"Milvus","optional":false},{"name":"NLP","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Weaviate","optional":false}],"status":"live","first_seen_at":"2026-10-07T12:17:49Z","employer_posted_date":null,"last_verified_at":"2026-10-07T12:17:49Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"Job Description :\n\nAs 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 techniques.\n\nYou 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 applications.\n\nThe role requires a combination of deep hands-on technical expertise, architecture capability, delivery ownership, and team leadership.\n\nKey Responsibilities:\n\n- Own the technical architecture and delivery of enterprise GenAI/NLP platforms and applications.\n\n- Define solution strategies for complex use cases involving RAG, conversational AI, document intelligence, knowledge assistants, information extraction, summarization, and enterprise search.\n\n- Design scalable end-to-end architectures covering data ingestion, document processing, NLP pipelines, embeddings, vector/hybrid search, LLM orchestration, APIs, security, monitoring, and deployment.\n\n- Evaluate and recommend LLMs, embedding models, vector databases, retrieval approaches, orchestration frameworks, and deployment architectures based on business requirements.\n\n- Establish enterprise patterns for RAG architecture, prompt engineering, LLM evaluation, model selection, grounding, guardrails, and responsible AI.\n\n- Lead the development of reusable GenAI components, frameworks, accelerators, and reference architectures.\n\n- Drive optimization of AI solutions across quality, latency, scalability, reliability, security, and infrastructure/LLM cost.\n\n- Define evaluation strategies and benchmarks for LLM and RAG applications, including retrieval quality, factuality, relevance, hallucination, and business-task accuracy.\n\n- Provide technical direction on frameworks such as Hugging Face, LangChain, LlamaIndex, and other emerging GenAI technologies.\n\n- Guide architecture and implementation of vector-search platforms using FAISS, Pinecone, Weaviate, Milvus, or equivalent technologies.\n\n- Lead the integration of AI solutions with enterprise APIs, data platforms, applications, and cloud infrastructure.\n\n- Establish engineering practices around API design, testing, CI/CD, observability, security, MLOps, and production readiness.\n\n- Mentor senior developers and engineers, conduct design/code reviews, and build technical capability within the team.\n\n- Work directly with architects, engineering leaders, product stakeholders, and clients to understand business problems and shape AI solutions.\n\n- Lead technical discussions, solution presentations, POCs, estimations, and architecture reviews.\n\n- Identify emerging GenAI technologies and assess their applicability to enterprise use cases.\n\n- Take ownership of technical risks, architectural decisions, and resolution of complex production issues.\n\n- Drive multiple workstreams and ensure delivery against quality, timeline, and business objectives.\n\nRequired Skills :\n\n- Expert-level Python development skills with strong knowledge of software architecture, APIs, concurrency, performance, and scalable application design.\n\n- Strong hands-on experience architecting and delivering LLM/GenAI and NLP solutions in production.\n\n- Deep expertise in RAG, LLM orchestration, embeddings, semantic/hybrid search, information retrieval, prompt engineering, and LLM evaluation.\n\n- Strong understanding of NLP pipelines, document processing, information extraction, classification, summarization, and conversational AI.\n\n- Extensive experience with Hugging Face, LangChain, LlamaIndex, or comparable LLM/NLP ecosystems.\n\n- Strong knowledge of vector databases such as FAISS, Pinecone, Weaviate, and Milvus.\n\n- Experience designing Python-based services and AI platforms using FastAPI/Flask and microservices architectures.\n\n- Strong understanding of cloud-native application development and production engineering.\n\n- Experience with Docker, Kubernetes, CI/CD, MLOps, monitoring, and observability.\n\n- Strong understanding of AI system security, data privacy, access control, and responsible AI considerations.\n\n- Excellent problem-solving, technical communication, stakeholder management, and leadership skills.\nSkills\nGenerative AI, Artificial Intelligence, LLM, NLP, Python, Deep 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