Job Description
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
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
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