AI Architect
Listed on 2026-07-01
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IT/Tech
AI Engineer (Applied/Software)
AI Architect
The AI Enablement team is seeking an AI Architect – Agentic Platforms to define the architectural foundations that power client's enterprise agent ecosystem. This role is responsible for designing and governing the architecture for agent-based integrations, agent registries, scoring/evals infrastructure, grounding patterns, and multi-agent orchestration platforms. The AI Architect provides deep technical leadership across engineering, product, data science, security, and cloud teams to ensure that agents are built safely, consistently, and with enterprise-grade reliability, performance, and observability.
This role combines expertise in large-scale AI systems, distributed cloud architecture, and modern agentic frameworks.
- Experience in cloud and distributed systems architecture focused on scalability, reliability, observability, and performance.
- Designing enterprise AI/ML systems; 1+ years hands-on with GenAI, agentic workflows, RAG, LLM-based integrations, or multi-agent systems.
- Strong expertise with agentic frameworks and tooling (MCP, Lang Chain, Lang Graph,Llama Index, autogen, crewai, Agent sdk,OpenAI SDK etc).
- Hands-on experience in modern software development and engineering practices.
- Proven experience integrating APIs and enterprise systems into agentic platforms and workflows.
- Ability to rapidly build AI-driven prototypes, proofs of concept, and demo-ready product experiences.
- Experience defining and governing enterprise architecture standards, patterns, and reference architectures.
- Deep understanding of MCP servers, tool calling, registries, eval pipelines, agent observability, and multi-agent orchestration.
- Hands-on experience with Azure and GCP, including Kubernetes, containerization, identity, networking, CI/CD, and API platforms.
- Familiarity with AIOps/MLOps stacks (MLflow, model registries, vector DBs, semantic layers, feature stores, monitoring).
- Strong knowledge of security, compliance, risk, and Responsible AI (RAI) considerations for enterprise agent systems.
- Demonstrated ability to partner across engineering, data science, product, and security teams to deliver complex AI platform architectures.
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