Senior Architect : AI

Senior AI Architect (GenAI, LLM, Agentic Systems)

Experience: 10+ years overall, 3–5+ years in hands‑on AI/LLM/GenAI

We are looking for a Senior AI Architect in India with strong hands‑on experience in Generative AI (GenAI) , Large Language Models (LLMs) , agentic AI systems , and enterprise‑scale retrieval architectures .

This role focuses on building production‑ready GenAI solutions , including RAG pipelines , LLM‑powered chatbots , multi‑agent AI workflows , and LLM orchestration using LangChain and LangGraph .

You will play a key role in defining AI architecture, GenAI strategy, and enterprise AI transformation initiatives .

Key Responsibilities – Generative AI & LLM Architecture

• Architect and deliver Generative AI solutions using LLMs, RAG architectures, and agentic AI frameworks
• Build end‑to‑end GenAI pipelines for:
• Retrieval‑Augmented Generation (RAG)
• LLM‑based search and conversational AI
• Autonomous agents and multi‑agent systems
• Develop and optimize LLM orchestration workflows using LangChain, LangGraph , and similar frameworks
• Design and scale enterprise retrieval systems , including:
• Embeddings generation
• Intelligent chunking strategies
• Vector indexing and semantic search
• Reranking for accuracy and relevance
• Work hands‑on with vector databases such as Pinecone, PGVector, Azure AI Search, Amazon Titan
• Lead prompt engineering , prompt optimization, evaluation, benchmarking, and LLM fine‑tuning
• Design secure, scalable, and compliant AI architectures aligned with enterprise IT and data platforms
• Integrate LLMs and GenAI APIs into enterprise applications using microservices and APIs
• Implement GenAIOps / LLMOps / MLOps practices , including CI/CD, monitoring, guardrails, and continuous improvement
• Collaborate with data scientists, ML engineers, architects, product managers, and business stakeholders

Required Skills & Experience – GenAI, LLM, Agentic AI

Generative AI & LLM Expertise

• Strong hands‑on experience with Generative AI and Large Language Models (LLMs)
• Experience working with OpenAI GPT‑4o , Claude , Llama , and open‑source LLMs
• Deep understanding of RAG architecture , LLM‑based summarization, search, and content generation
• Expertise in prompt engineering , prompt tuning, and LLM optimization

Agentic AI & Orchestration

• Hands‑on experience building agentic AI systems
• Experience creating tool‑calling agents, autonomous agents, and multi‑agent workflows
• Strong experience with LangChain, LangGraph , or similar AI orchestration frameworks

Vector Databases & Retrieval Systems

• Experience with vector databases and semantic search platforms , including:
• Pinecone
• PGVector
• Azure AI Search
• Amazon Titan
• Strong knowledge of embedding models, chunking strategies, indexing, and retrieval optimization

Cloud & Enterprise AI Architecture

• Experience deploying GenAI solutions on cloud platforms :
• AWS (SageMaker, Bedrock)
• Azure (Azure ML, Azure OpenAI)
• Google Cloud (Vertex AI)
• Strong understanding of enterprise AI architecture, security, governance, and responsible AI
• Experience integrating AI solutions into large‑scale enterprise systems

MLOps / GenAIOps

• Experience with LLMOps / GenAIOps pipelines
• Knowledge of CI/CD for LLM workflows , evaluation frameworks, monitoring, and AI safety layers

Collaboration & Leadership

• Ability to collaborate across AI research, ML engineering, data, product, and business teams
• Strong communication skills to explain GenAI architecture and LLM design decisions
• Experience influencing AI strategy and architecture in enterprise environments