Sr. AI Architect

Summary

Job Description

The AI Enablement team at Client is seeking an AI Architect – Agentic Platforms to define the architectural foundations that power 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.

• 10+ years’ experience in cloud and distributed systems architecture focused on scalability, reliability, observability, and performance.
• 7+ years 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.

• Define and maintain the enterprise reference architecture for agentic platforms (agentic framework, tools, MCP, registries, evals, orchestration, grounding, observability).
• Establish architectural standards and best practices for agent design, tool integration, safety, telemetry, versioning, and lifecycle management.
• Provide architectural leadership for agentic platform engineering teams, ensuring scalability, resiliency, performance, and operability.
• Design and guide integration with semantic layers, embeddings, vector search, knowledge models, and enterprise data products to enable grounded agent behavior.
• Drive architectural direction for low‑code/no‑code agent‑building platforms, ensuring governance, consistency, and ease of adoption.
• Partner with cloud, security, product, and enterprise architecture teams to align agentic platform designs with AI governance and RAI principles.
• Define and support agentic SDLC through patterns for evals, safety tests, regression gates, monitoring, and benchmarking.
• Evaluate new agentic frameworks, open‑source standards, and orchestration tools to guide build‑vs‑buy platform decisions.
• Provide hands‑on architectural guidance across engineering and data science teams, enabling scalable, secure, and cost‑efficient agent deployment.
• Translate complex business needs into clear technical design patterns and platform capabilities that accelerate agent development.

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