Principal Agentic AI Platform Architect

Job Summary

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.

1. Agentic AI Platform Architecture

• 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.

2. Agent Orchestration and Framework Engineering

• 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.

3. RAG and Document Intelligence

• 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.

4. AI Observability, Security, and Governance

• 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.

5. Cloud-Native and Distributed Systems Engineering

• 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.

6. Technical Leadership and Stakeholder Management

• 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.

7. Portfolio and Program Governance

• 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.

Required Technical Skills

• 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.

Preferred Experience

• 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.