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.

Skills:
architecture,integration,ai,enterprise

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