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 techniques.

You 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.

The role requires a combination of deep hands-on technical expertise, architecture capability, delivery ownership, and team leadership.

Key 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 objectives.

Required 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.