Overview
Designs and builds new solutions from idea to a proven, secure, working product, with an emphasis on building, proving, and learning.
Key Responsibilities
- Work with business sponsors to turn high-level ideas into framed technical problems, candidate designs, and a realistic path to a first working version.
- Build Proofs of Concept (PoC) and Minimum Viable Products (MVP) that validate concepts quickly and surface the technical risks worth knowing about before further investment.
- Evaluate emerging platforms, frameworks, and AI models against specific institutional problems, and present findings with cost, risk, and adoption implications.
- Apply a fail-fast approach, retiring ideas that do not prove out and documenting the learning so it benefits future initiatives.
- Prepare successful experiments for transition to product teams, covering design documentation, operational readiness, and handover.
- Contribute reusable patterns, accelerators, and components that shorten the path from idea to working software.
- Design and build solutions on the Microsoft Power Platform, using Power Apps for canvas and model-driven applications, Power Automate for workflow and process automation, Power Pages for external-facing experiences, and Copilot Studio for conversational and agentic interfaces.
- Build Microsoft Dataverse data models, covering tables and relationships, business rules, calculated and rollup columns, alternate keys, and choice sets, with attention to performance and maintainability.
- Configure the Dataverse security model through business units, security roles, teams, field-level security, and hierarchy security, ensuring least-privilege access to sensitive institutional data.
- Extend the platform where declarative configuration is insufficient, using plug-ins, custom Application Programming Interfaces (API), Power Apps Component Framework (PCF) controls, and custom connectors.
- Connect Dataverse to the wider estate using the Dataverse Web API, virtual tables, Microsoft Fabric and Synapse Link, and Azure integration services.
- Apply Application Lifecycle Management (ALM) practice on the platform, including solution layering, managed and unmanaged solutions, environment strategy, and automated pipelines.
- Judge when a requirement is best met by low-code configuration and when it warrants a pro-code approach, and explain that trade-off to sponsors.
- Approach design on an AI-first basis, assessing where AI can materially change the shape of a solution rather than simply decorate it.
- Build Agentic AI solutions in which autonomous agents plan, invoke tools and enterprise systems, maintain state across steps, and hand off to other agents or to a human, using Microsoft Copilot Studio, Microsoft 365 Copilot extensibility, Azure AI Foundry, and Semantic Kernel.
- Build Retrieval-Augmented Generation (RAG) solutions that ground models in institutional and domain-specific sources, including Dataverse content.
- Apply traditional Machine Learning (ML) and predictive techniques where these fit the problem better than a generative or agentic approach.
- Test AI solutions for accuracy, hallucination risk, bias, cost per transaction, and safe tool invocation, and implement guardrails and human oversight of agent actions.
- Ensure AI solutions follow the World Bank Group's responsible AI, data privacy, and information security requirements.
- Build secure applications on Microsoft Azure, applying cloud-native patterns including serverless functions, containers, event-driven design, and API management.
- Contribute to solutions hosted on Amazon Web Services (AWS) where a workload sits there.
- Apply security by design across identity, secrets management, network isolation, and data protection at rest and in transit, and work with the Information Security team to meet its requirements and remediate findings.
- Integrate new solutions with existing World Bank Group systems and data platforms so that experiments do not become isolated digital silos.
- Document and explain designs, decisions, and trade-offs clearly to both technical and non-technical audiences, and share emerging practice with engineering colleagues.
Required Experience
- Master's degree in Computer Science or a related Science, Technology, Engineering and Mathematics (STEM) field, for example Engineering, Mathematics, or Information Systems, with 5 years of relevant experience; or a bachelor's degree with a minimum of 8 years of relevant experience.
- Demonstrated experience building applications from idea through to a working solution, with at least two examples the candidate personally designed and built.
- Strong hands-on engineering capability, with the proven ability to write, review, and troubleshoot production code.
- Sound, practical experience with the Microsoft Power Platform across Power Apps, Power Automate, Power Pages, and Copilot Studio, evidenced by solutions delivered into live use.
- Solid Microsoft Dataverse skills spanning data modelling, the security model, extensibility through plug-ins and custom connectors, integration, and Application Lifecycle Management (ALM) through solutions and environment strategy.
- Practical experience building and securing applications on Microsoft Azure, including compute, serverless functions, secrets management, application monitoring, and identity through Microsoft Entra Identity, formerly Azure Active Directory (Azure AD).
- Working knowledge of Amazon Web Services (AWS) core compute, storage, serverless, and identity services sufficient to contribute to workloads hosted there.
- Experience developing solutions in a modern enterprise stack, for example Microsoft .NET and C#, Python, or JavaScript and TypeScript frameworks, together with RESTful Application Programming Interfaces (API).
- Working understanding of modern design patterns including microservices, event-driven design, containers, and API gateways.
- Demonstrated experience building AI solutions, with specific evidence of Agentic AI work alongside Retrieval-Augmented Generation (RAG) and, ideally, traditional Machine Learning (ML).
- Familiarity with Microsoft AI services including Azure OpenAI Service, Azure AI Foundry, and Copilot Studio; awareness of Amazon Bedrock is a plus.
- Experience with engineering best practices, including version control, Continuous Integration and Continuous Delivery (CI/CD), Infrastructure as Code (IaC), automated testing, logging, and monitoring. Hands-on DevOps experience is required.
- Familiarity with modern data platforms, including Microsoft Fabric, Power BI, and Extract, Transform and Load (ETL) or pipeline tooling, is a plus.
- Ability to explain technical concepts and demonstrate working prototypes persuasively to non-technical stakeholders.
- Ability to learn new technologies quickly; a self-starter who is motivated, comfortable with ambiguity, and communicates effectively.
- Proven ability to collaborate across boundaries and contribute productively to team output, demonstrating respect for different points of view.
- Ability to take personal responsibility and accountability for timely response to client queries, requests, or needs, working to remove obstacles that may impede execution or overall success.
Qualifications
- Master's degree in Computer Science or a related Science, Technology, Engineering and Mathematics (STEM) field, for example Engineering, Mathematics, or Information Systems, with 5 years of relevant experience; or a bachelor's degree with a minimum of 8 years of relevant experience.
- Recommended certifications include Microsoft Certified Power Platform Developer Associate, Power Platform Solution Architect Expert, Azure Developer Associate, and Azure AI Engineer Associate.
