Senior AI Architect

At the World Bank, a member of the World Bank Group (WBG), you’ll join a diverse, global community working across cultures, disciplines, and borders to address the world’s most pressing development challenges. With 1.2 billion young people reaching working age in the decade ahead, the challenge of job creation has never been greater. Through partnerships across 145 countries and more than 182 offices worldwide, we work with governments, private sector, development partners and other stakeholders to invest in people, strengthen markets, and deliver scalable, data-driven solutions that generate more and better jobs and improve lives. For more information, visit https://www.worldbank.org/en/who-we-are/ibrd ITS Vice Presidency Context The Information and Technology Solutions (ITS) Vice Presidential Unit (VPU) enables the World Bank Group to achieve its mission of ending extreme poverty and boost shared prosperity on a livable planet by delivering transformative information and technologies to its staff working in over 150+ locations. For more information on ITS, see this video: https://www.youtube.com/watch?reload=9 v=VTFGffa1Y7w Unit Context The ITS Operations Products unit (ITSOP) enables the Bank's business processes through modern, flexible, and user-friendly digital platforms that integrate and optimize critical functions for front-line staff. ITSOP also drives AI-powered business solutions that support operational priorities and accelerate trusted, data-informed decision-making. ITSOP is seeking an Associate Senior AI Solution Architect to lead the unit’s AI strategy and translate it into secure, scalable, and production-ready solutions. The successful candidate will personally design and prototype AI components, develop reference implementations, evaluate technical approaches, and guide engineering teams through deployment and operationalization. This is not a purely advisory role. It requires recent, demonstrable experience building and operating Generative AI and agentic solutions in enterprise environments. Key Responsibilities AI Strategy and Architecture • Develop the ITSOP AI strategy, capability roadmap, reference architectures, standards, and reusable patterns, aligned with enterprise direction and measurable operational outcomes. • Translate business opportunities into prioritized AI use cases, target architectures, delivery approaches, and investment recommendations. • Design end-to-end solutions spanning user experience, agents, models, retrieval, enterprise data, APIs, identity, security, observability, and operational support. • Evaluate technology choices and architecture trade-offs across models, platforms, orchestration frameworks, retrieval approaches, and integration patterns. Hands-on Prototyping and Engineering • Design, code, test, and demonstrate working prototypes and reference implementations using Python and modern software engineering practices. • Graduate selected solutions from discovery and proof of concept through technical validation, production design, deployment, and operational readiness. • Create reusable SDKs, templates, components, and implementation guidance that enable delivery teams to build consistently and securely. • Conduct design and code reviews, troubleshoot complex cross-layer issues, and provide practical technical direction to engineering teams. Agentic AI, Enterprise Knowledge, and Integration • Design and implement agentic solutions using tool calling, structured outputs, orchestration, state and memory management, routing, retries, fallbacks, and human approval patterns. • Design safe single-agent and multi-agent workflows with clear task boundaries, controlled permissions, escalation paths, and failure handling. • Build Retrieval-Augmented Generation and enterprise knowledge solutions using semantic search, vector retrieval, knowledge graphs, ontologies, and structured and unstructured data. • Integrate agents with enterprise applications, APIs, event-driven services, data platforms, content repositories, and business workflows. • Apply open agent interoperability approaches such as Model Context Protocol (MCP), Agent2Agent (A2A), or equivalent standards where appropriate. Production Quality, Security, and AI Operations • Define and implement evaluation frameworks covering groundedness, relevance, completeness, task success, safety, latency, reliability, and cost. • Create automated regression tests, representative evaluation datasets, and human-review processes for prompts, retrieval, tools, models, and agent behavior. • Implement end-to-end tracing, prompt and configuration versioning, model and tool telemetry, cost monitoring, feedback capture, release controls, and rollback approaches. • Apply AI-specific security controls for prompt injection, data leakage, unsafe tool execution, sensitive information, identity, authorization, auditability, and workload isolation. • Embed Responsible AI, privacy, records management, compliance, and human oversight requirements throughout the solution lifecycle. Technical Leadership and Adoption • Act as a trusted technical advisor while remaining directly involved in prototyping, implementation decisions, and production problem-solving. • Partner with business, product, architecture, data, security, platform, and engineering teams to deliver measurable value and sustainable adoption. • Mentor architects and engineers, share implementation practices, and raise the unit’s capability to deliver governed AI solutions. • Monitor emerging AI technologies and recommend adoption only where they provide clear business, engineering, or operational value.