Senior architect : ai

Senior AI Architect (Gen AI, LLM, Agentic Systems)
Experience: 10+ years overall, 3–5+ years in hands‑on AI/LLM/Gen AI
We are looking for a Senior AI Architect in India with strong hands‑on experience in Generative AI (Gen AI), Large Language Models (LLMs), agentic AI systems, and enterprise‑scale retrieval architectures.
This role focuses on building production‑ready Gen AI solutions, including RAG pipelines, LLM‑powered chatbots, multi‑agent AI workflows, and LLM orchestration using Lang Chain and Lang Graph.
You will play a key role in defining AI architecture, Gen AI 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 Gen AI 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 Lang Chain, Lang Graph, 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 Gen AI APIs into enterprise applications using microservices and APIs
- Implement Gen AIOps / 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 – Gen AI, LLM, Agentic AI
Generative AI & LLM Expertise
- Strong hands‑on experience with Generative AI and Large Language Models (LLMs)
- Experience working with Open AI 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 Lang Chain, Lang Graph, 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 Gen AI solutions on cloud platforms:
- AWS (Sage Maker, Bedrock)
- Azure (Azure ML, Azure Open AI)
- 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 / Gen AIOps
- Experience with LLMOps / Gen AIOps 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 Gen AI architecture and LLM design decisions
- Experience influencing AI strategy and architecture in enterprise environments