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

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

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

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

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

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

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

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

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

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

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

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

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

Essential Skills

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

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Extensive experience designing enterprise GenAI architectures.

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

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Strong knowledge of AI agents and multi-agent architectures.

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Experience with LLM orchestration and agent frameworks.

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

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Expertise in prompt engineering and context engineering/orchestration.

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

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

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

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

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Strong stakeholder management, communication, and technical leadership skills.

Preferred / Desirable Skills

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

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

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

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Knowledge of Python and modern AI/ML frameworks.

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

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Experience working with enterprise security and data privacy requirements.

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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.

📌 GenAI Architect (Bengaluru)
🏢 Important Business
📍 Bengaluru