Generative AI Architect - Agentic AI Solutions

Job Description :GenAI Solutions Architect :Role Purpose :

Lead the architecture and delivery of enterprise-grade Generative AI and Agentic AI solutions, translating business requirements into scalable, secure, and production-ready AI platforms.

Own the technical vision from discovery and PoC through production deployment and optimization.

Key Responsibilities :

• Design end-to-end GenAI, RAG, Agentic AI and multi-agent architectures.

• Define solution patterns covering LLMs, embeddings, vector databases, orchestration, APIs, data pipelines and enterprise integrations.

• Architect AI agents with tool/function calling, workflow orchestration, memory, Human-in-the-Loop and MCP-based integrations.

• Evaluate and select appropriate foundation models, AI platforms and architecture patterns based on quality, latency, security and cost.

• Design solutions using Azure OpenAI / Azure AI Foundry, Amazon Bedrock / AgentCore, Google Vertex AI or equivalent platforms.

• Establish LLMOps, evaluation, observability, guardrails and Responsible AI practices.

• Define scalable cloud-native deployment architectures using APIs, containers, Kubernetes, serverless and asynchronous/event-driven patterns.

• Ensure architecture meets enterprise requirements for security, privacy, resilience, scalability, compliance and cost optimization.

• Lead architecture workshops, technical design reviews, PoCs and solution demonstrations.

• Create reference architectures, technical standards and reusable accelerators for GenAI adoption.

• Partner with business, product, security, data and engineering stakeholders to drive solutions from use case identification to production adoption.

Required Skills :

• Strong expertise in Generative AI, LLMs, Agentic AI and AI solution architecture.

• Hands-on experience with RAG, embeddings, vector search and semantic retrieval.

• Experience with agent frameworks such as LangGraph, LangChain, Microsoft Agent Framework, Semantic Kernel, Bedrock Agents/AgentCore or equivalent.

• Strong understanding of multi-agent orchestration, tool calling, context engineering, memory and Human-in-the-Loop patterns.

• Experience with one or more cloud AI ecosystems: Azure, AWS or GCP.

• Strong knowledge of Python, REST APIs, microservices and distributed/cloud-native architectures.

• Experience with LLM evaluation, tracing, monitoring, prompt/model versioning and LLMOps/MLOps.