Principal Agentic AI Platform Architect

We are seeking a Principal Agentic AI Platform Architect to lead the architecture design and engineering of an enterprise-grade Agentic AI Harness for banking. The role will focus on building a scalable extensible AI platform capable of supporting 50 enterprise AI use cases through reusable agent frameworks orchestration services tool integrations context and memory management document intelligence observability security and governance.

• Lead the end-to-end architecture and engineering of an enterprise Agentic AI Harness for banking.

• Evolve existing agent frameworks into scalable extensible reusable and production-ready platform capabilities.

• Design architectures supporting multiple AI use cases business domains and enterprise integrations.

• Define architectural standards reference patterns reusable components and platform roadmaps.

• Design and optimize agent orchestration planning reasoning tool calling autonomous workflows and multi-agent coordination.

• Build reusable tool libraries agent services and integration frameworks.

• Architect context management memory management retrieval and state-management capabilities.

• Evaluate and implement agentic frameworks such as Google ADK LangGraph LangChain Semantic Kernel CrewAI and AutoGen.

• Architect Retrieval-Augmented Generation (RAG) solutions using embeddings vector databases and enterprise knowledge systems.

• Design document intelligence capabilities for enterprise information retrieval and processing.

• Establish reusable retrieval pipelines knowledge integration patterns and context-grounding mechanisms.

• Design AI observability and monitoring frameworks to track agent execution performance reliability and operational health.

• Establish evaluation frameworks for assessing model outputs agent behavior and overall system quality.

• Implement governance controls security standards access controls and responsible AI practices.

• Address model risk data protection traceability and enterprise compliance requirements.

• Apply strong software engineering principles across APIs microservices distributed systems and cloud-native architectures.

• Design scalable platform services integration layers and reusable engineering components.

• Ensure platform extensibility reliability performance maintainability and operational readiness.

• Provide hands-on architectural guidance to AI engineers software engineers and platform teams.

• Mentor engineering teams and establish engineering best practices.

• Collaborate with C-suite leaders enterprise architects business stakeholders risk teams and technology leadership.

• Evaluate emerging AI technologies and recommend strategic platform improvements.

• Provide executive-level reporting on delivery progress financial position risks resource constraints outcomes and required actions.

• Coordinate across program finance technology business risk control and vendor teams to validate data and resolve issues.

• Recommend initiative priorities and trade-offs based on strategic value funding capacity risk and readiness.

• Standardize portfolio reporting and prepare executive dashboards presentations and briefing materials.

• Experience: 12 years of software engineering experience including 5 years designing and building enterprise AI/ML platforms.

• Agentic AI Architecture: Proven experience designing and delivering enterprise agentic AI systems AI platforms or multi-agent frameworks.

• Agentic AI Concepts: Deep understanding of orchestration planning reasoning tool calling memory management retrieval and autonomous workflows.

• Frameworks: Hands-on expertise with Google ADK LangGraph LangChain Semantic Kernel CrewAI AutoGen or equivalent frameworks.

• AI Platform Engineering: Experience building orchestration layers reusable tool libraries context-management frameworks and extensible platform services.

• RAG and Vector Search: Expertise in embeddings vector databases retrieval pipelines and enterprise knowledge systems.

• Document Intelligence: Experience architecting document-processing and knowledge-retrieval capabilities.

• Software Engineering: Strong background in cloud-native systems APIs microservices distributed systems and platform engineering.

• AI Operations and Governance: Knowledge of AI observability model evaluation monitoring security governance and responsible AI principles.

• Technical Leadership: Ability to influence senior technology stakeholders and lead architecture decisions across engineering teams.

• Experience delivering enterprise AI platforms within banking or financial services.

• Experience supporting multiple business domains through shared AI platform capabilities.

• Experience defining enterprise architecture standards technology roadmaps and platform adoption strategies.

• Experience communicating technical strategy delivery risks and investment trade-offs to executive stakeholders.