The ideal candidate will be responsible for working cross-functionally to understand architecture needs by multiple business units. To be effective in this position, you must feel comfortable owning the entire architecture development process from inception to completion.

Responsibilities

As AI Architect, you will define the enterprise AI platform architecture supporting the full Product Development Lifecycle and design multi-agent systems for requirements engineering, product management, software development, code review, testing, DevSecOps, documentation, and release engineering. You will build reusable AI services, APIs, SDKs, and agent frameworks; architect RAG, knowledge graph, memory, and context-management layers; and establish LLM orchestration and model-routing strategies.

You will lead AI governance across security, responsible AI, guardrails, prompt management, evaluation frameworks, and human-in-the-loop processes. Partnering with Product, Engineering, Security, and Enterprise Architecture teams, you will evaluate emerging AI technologies and define enterprise standards.

Hands-on responsibilities include designing and deploying Claude-native agents and subagents using Claude Code, Skills (SKILL.md),



and project rules (CLAUDE.md); building MCP servers and FastAPI endpoints that expose internal tools and data as agent-callable services; and implementing multi-agent orchestration patterns such as supervisor/worker, planner/executor, reflection, and handoff across the platform.

Qualifications

- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- Over 10 years of data engineering experience that includes significant team leadership or management exposure
- Over 4 years building AI/ML or GenAI solutions.
- Should have robust knowledge of LLMs, RAG, agentic AI, MCP, vector databases, and AI evaluation.
- Hands-on experience across at least one major cloud AI platform such as Azure AI Foundry, Azure OpenAI, AWS Bedrock, or Google Vertex AI.
- Strong experience with Kubernetes, Docker, and cloud-native architectures.
- Familiarity with agent frameworks such as LangGraph, Semantic Kernel, AutoGen, CrewAI, or the Microsoft Agent Framework.
- Strong grounding in API architecture and in integrating AI into enterprise SDLC platforms.
- Hands-on experience with the Claude ecosystem
- Production experience with the Anthropic API — including tool use, structured outputs, streaming, and prompt caching — as well as strong Python skills, covering backend API development with FastAPI (or Starlette/Flask), async patterns, REST design, authentication, and API contracts that double as agent tools.

Location -Noida / Remote / Hybrid

📌 AI Solutions Architect (Noida)
🏢 Nuaav
📍 Noida