Sr. AI Architect

SUMMARY

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, LangChain, LangGraph,LlamaIndex, 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 Google Cloud Platform, 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.