GenAI Architect in Bangalore, भारत

Generative AI Architect

Experience: 10+ Years
Job Type: Full-Time
Domain: Generative AI / Artificial Intelligence

Role Overview

We are looking for an experienced Generative AI Architect to lead the design and architecture of enterprise-grade GenAI solutions. The ideal candidate will have strong expertise in LLMs, RAG, AI agents, multi-agent systems, prompt engineering, vector databases, LLM orchestration, and enterprise system integration .

The role will be responsible for defining end-to-end GenAI architecture, establishing AI design standards, ensuring responsible and secure AI adoption, and mentoring engineering teams in building scalable AI-first solutions.

Key Responsibilities

• Define and own the end-to-end GenAI architecture , including LLM orchestration, agent frameworks, and enterprise integration layers.

• Design scalable and modular GenAI solutions using LLMs, embeddings, vector databases, and retrieval mechanisms .

• Architect agent-based and multi-agent systems supporting autonomous reasoning, planning, and task execution.

• Establish reusable architecture patterns for RAG, conversational AI, document intelligence, and context-aware decision systems .

• Lead integration of GenAI solutions with enterprise applications using APIs, microservices, event-driven architectures, and secure data pipelines .

• Define prompt engineering strategies, prompt lifecycle management, and context orchestration mechanisms.

• Design and implement Responsible AI frameworks covering governance, explainability, auditability, security, and compliance.

• Design human-in-the-loop architectures , including confidence thresholds, feedback loops, checkpoints, and fallback mechanisms.

• Establish monitoring and observability frameworks covering model performance, hallucination detection, accuracy, drift, and cost optimization .

• Work closely with business and product stakeholders to identify high-value GenAI use cases and develop scalable transformation roadmaps.

• Drive standardization of GenAI architecture best practices, reusable components, and enterprise AI governance policies.

• Mentor and guide engineering teams on GenAI architecture, prompt engineering, LLMOps, and AI-first solution design .

Essential Skills

• Strong expertise in Generative AI and Large Language Models (LLMs) .

• Extensive experience designing enterprise GenAI architectures .

• Hands-on experience with RAG and retrieval-based AI systems .

• Strong knowledge of AI agents and multi-agent architectures .

• Experience with LLM orchestration and agent frameworks .

• Strong understanding of embeddings, vector databases, semantic search, and retrieval mechanisms .

• Expertise in prompt engineering and context engineering/orchestration .

• Experience integrating AI solutions through REST APIs, microservices, and event-driven architectures .

• Knowledge of LLMOps, AI observability, model evaluation, monitoring, and optimization .

• Strong understanding of Responsible AI, AI governance, security, compliance, and auditability .

• Experience designing scalable, secure, and production-ready AI solutions.

• Strong stakeholder management, communication, and technical leadership skills.

Preferred / Desirable Skills

• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform .

• Experience with frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or similar agent frameworks .

• Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, or Azure AI Search .

• Knowledge of Python and modern AI/ML frameworks .

• Experience with CI/CD, DevOps, MLOps, and LLMOps practices.

• Experience working with enterprise security and data privacy requirements.

• Experience leading enterprise-wide AI transformation initiatives.

Experience Required

10+ years of overall technology experience , with significant hands-on experience in AI/ML and Generative AI architecture .

Candidates should demonstrate experience delivering production-grade GenAI solutions , rather than only POCs or experimental projects.