
Short Term Consultant - Software Engineer (Applied AI)
The World Bank Group · Washington, DC
- Hybrid
- Temporary
- $100,000 / year
- Washington, DC
Job highlights
- Engineer AI platforms and infrastructure.
- Develop and maintain AI applications.
- Ensure AI security and responsible use.
- Collaborate with diverse teams.
- Improve AI systems and processes.
About the role
About the World Bank and eMBeD
The World Bank is a global development institution committed to reducing poverty and promoting shared prosperity. Through its Development Economics Vice Presidency (DEC), the Bank generates cutting-edge research and data to inform policy design and implementation worldwide. Within DEC, the Mind, Behavior, and Development Unit (eMBeD) applies insights from behavioral science to improve development outcomes. The unit works across sectors—including education, labor markets, public health, and digital services—to design, test, and scale interventions that improve decision-making and service delivery. Increasingly, eMBeD integrates artificial intelligence (AI) into its operational and research portfolio to accelerate learning, enhance service delivery, and support governments in adopting evidence-based and human-centered innovations.Role Purpose
The Software Engineer (Applied AI) is responsible for the production engineering of eMBeD’s AI-enabled platforms and for establishing the engineering substrate that enables non-engineering team members to contribute safely and effectively at scale. The role combines hands-on systems engineering, AI application development, and institutional capacity building, ensuring that AI solutions are robust, secure, and aligned with responsible AI standards within a development context.Duties and Accountabilities
The Software Engineer (Applied AI) will:- Perform advanced-level engineering work on AI-enabled applications with limited supervision, including system architecture design, production deployment, and operational ownership.
- Propose and implement solutions to moderately complex technical challenges in applied AI engineering, including agentic memory design, Model Context Protocol (MCP) server architecture, and evaluation infrastructure.
- Translate engineering trade-offs into clear, outcome-oriented language for non-technical stakeholders, including unit leadership and behavioral science teams.
- Provide technical guidance, code review, and architectural input to behavioral scientists and economists working in AI-assisted development environments.
- Serve as a technical resource for security, reliability, and responsible AI considerations across the unit’s portfolio.
Key Responsibilities
1. Production Engineering for AI Applications- Design, implement, and maintain MCP server infrastructure and reusable agentic patterns supporting eMBeD’s AI-enabled applications.
- Manage cloud infrastructure, container orchestration, CI/CD pipelines, and deployment environments across development, staging, and production.
- Ensure scalability, reliability, and performance of deployed AI applications.
Establish and enforce engineering standards for AI-assisted development, including:
- Code review protocols
- Automated testing frameworks
- Evaluation harnesses
- Security checklists
- Golden datasets
- LLM-as-judge pipelines with bias controls (verbosity, position, sycophancy)
- Human-in-the-loop review workflows
Implement security controls across the AI application lifecycle, including authentication, role-based access control, secrets management, and encryption. Design and test safeguards against AI-specific threats such as prompt injection, data exfiltration, and model misuse. Implement guardrails, output validation mechanisms, and content filtering aligned with responsible AI principles. Ensure compliance with data governance standards, including handling of sensitive data, third-party API integrations, retention policies, and audit logging. 4. Cross-functional Collaboration and Knowledge Transfer
Collaborate closely with behavioral scientists, economists, and operational teams to translate research and policy needs into technical systems. Enable non-engineering staff to contribute safely to AI-assisted development through onboarding, tooling support, and best practices. Communicate technical decisions, risks, and trade-offs through clear documentation suited to an asynchronous, knowledge-driven environment. 5. Continuous Improvement and Innovation
Evaluate emerging AI technologies, agentic architectures, and knowledge system approaches beyond traditional retrieval-augmented generation. Identify and address technical debt, balancing rapid delivery with long-term system sustainability. Maintain awareness of the evolving AI and engineering landscape, proactively introducing tools and practices that enhance productivity and output quality.
Selection Criteria
1. Education and ExperienceMaster’s degree in Computer Science, Software Engineering, Information Systems, or a related field with at least 5 years of relevant experience; or Bachelor’s degree with at least 7 years of relevant experience; or equivalent combination of education and experience. Demonstrated track record of taking AI applications from prototype to production at scale. 2. Backend and Systems Engineering
Strong proficiency in Python and Node.js or TypeScript. Experience designing and operating production APIs (REST, GraphQL, MCP servers). Solid understanding of distributed systems (queues, retries, idempotency, asynchronous processing). Experience with relational and non-relational databases, including vector stores. Proven ability to convert prototypes into production-grade systems. 3. Cloud Architecture and Production Operations
Deep experience with cloud platforms (AWS, Azure, or GCP), including containerization, orchestration, autoscaling, and CI/CD pipelines. Strong observability practices (logging, metrics, distributed tracing). Experience managing production systems, including incident response, capacity planning, and cost optimization. Preference for managed, scalable infrastructure in lean team environments. 4. Applied AI and Knowledge Systems
Hands-on experience integrating LLM APIs (e.g., OpenAI, Anthropic, open-source models via vLLM). Strong expertise in context engineering: Retrieval, Agentic memory (working, episodic, semantic), Prompt caching and long-context strategies. Familiarity with hybrid search, knowledge graphs, and advanced knowledge systems. Experience designing evaluation frameworks (gold datasets, LLM-as-judge, uncertainty estimation). 5. AI Security and Responsible AI
Experience mitigating AI-specific risks (prompt injection, exfiltration, misuse). Knowledge of guardrails, structured outputs, and content filtering. Exposure to red-teaming AI systems or contributing to AI risk assessments. Familiarity with responsible AI principles, including transparency and calibration. 6. Data Governance and Privacy
Knowledge of data protection frameworks (e.g., GDPR, OECD principles). Experience handling PII, implementing data minimization strategies, and designing retention policies. Ability to assess third-party API data flows and implement audit mechanisms. 7. AI-Assisted Development and Code Review
Daily fluency with agentic coding tools (e.g., Claude Code, Cursor, Codex CLI). Strong ability to rigorously review AI-generated code. Experience working in environments where non-engineers contribute code, with a demonstrated ability to enable contributions without compromising quality. Balance between rapid iteration and disciplined engineering standards. 8. Cross-functional Collaboration
Experience working in multidisciplinary teams (research, policy, or operations). Excellent written communication skills in document-driven environments. Ability to translate technical trade-offs into outcomes-oriented language for senior stakeholders. Track record of producing technical documentation, runbooks, and decision records.
Deliverables
The Software Engineer (Applied AI) is expected to deliver:- Production-ready AI-enabled platforms and infrastructure
- Engineering standards and documentation frameworks
- Secure, compliant, and scalable AI systems
- Evaluation pipelines and quality assurance mechanisms
- Capacity-building support for non-engineering contributors
Reporting and Collaboration
The consultant will work under the supervision of the eMBeD leadership team and collaborate closely with behavioral scientists, economists, and operational teams across the World Bank. Please submit your CV and cover letter to embed@worldbank.org by May 15, 2026.Skills & topics
- Software Engineer
- Applied AI
- Artificial Intelligence
- Python
- Node.js
- Cloud Computing
- AWS
- Azure
- GCP
- CI/CD
- LLM
- Machine Learning
- Backend Development
- Systems Engineering
- Production Engineering
- Data Governance
- Responsible AI
- Consultant
- World Bank
How to get hired
- Tailor your CV: Highlight AI, Python, Node.js, cloud, and production engineering experience.
- Craft a compelling cover letter: Emphasize your track record in taking AI applications to production.
- Showcase your skills: Demonstrate expertise in cloud platforms, CI/CD, and LLM integration.
- Prepare for technical interviews: Be ready to discuss distributed systems and AI security.
- Understand the context: Research the World Bank's mission and eMBeD's role in development economics.
Technical preparation
Master Python and Node.js/TypeScript.,Practice cloud deployment and CI/CD.,Study LLM integration and prompting.,Prepare for distributed systems questions.
Behavioral questions
How do you translate technical concepts for non-technical people?,Describe a time you took an AI project from prototype to production.,How do you balance rapid iteration with engineering standards?,How do you ensure security and responsible AI practices?
Frequently asked questions
- What is the application deadline for the Software Engineer (Applied AI) role at The World Bank?
- The application deadline for the Software Engineer (Applied AI) position at The World Bank is May 15, 2026. Please submit your CV and cover letter to embed@worldbank.org.
- What specific AI technologies is The World Bank looking for in this Software Engineer role?
- The World Bank is seeking expertise in applied AI, including experience with LLM APIs (OpenAI, Anthropic, vLLM), agentic memory, prompt engineering, and evaluation frameworks like LLM-as-judge pipelines.
- What programming languages are essential for this Software Engineer (Applied AI) position?
- Strong proficiency in Python and Node.js or TypeScript is essential for this role, particularly for backend and systems engineering tasks related to AI applications.
- Does this Software Engineer role require experience with cloud platforms?
- Yes, deep experience with cloud platforms such as AWS, Azure, or GCP is required, including containerization, orchestration, autoscaling, and CI/CD pipelines.
- What is the expected experience level for this Software Engineer (Applied AI) position?
- Candidates should have a Master's degree and at least 5 years of relevant experience, or a Bachelor's degree with at least 7 years of relevant experience in software engineering and applied AI.
- What are the key deliverables for the Software Engineer (Applied AI) consultant?
- Key deliverables include production-ready AI platforms and infrastructure, engineering standards documentation, secure and scalable AI systems, evaluation pipelines, and capacity-building support for non-engineering contributors.
- How does The World Bank ensure responsible AI in this role?
- The role emphasizes implementing security controls, mitigating AI-specific risks, designing guardrails and output validation, and ensuring compliance with data governance and responsible AI principles.
- Is this a remote or on-site position?
- The job description does not explicitly state the work arrangement. However, given the nature of consulting roles at international organizations, it is likely to be based in a specific location, with potential for some flexibility. Further clarification would be needed from the hiring team.