Associate Senior AI Solutions Officer

The Associate Senior AI Solutions Officer will design, develop, implement, and operationalize AI-powered business solutions to improve operational effectiveness and support World Bank development outcomes. The role involves collaboration with business stakeholders and technical teams to deliver innovative AI capabilities as part of the World Bank Group’s AI and digital transformation agenda.

Responsibilities

• Design, build, and deploy AI-powered solutions that address business needs and improve operational outcomes.
• Translate business requirements into scalable, secure, and production-ready AI applications.
• Develop prototypes, pilots, and enterprise-grade solutions using Generative AI, Agentic AI, machine learning, and automation technologies.
• Deliver AI solutions supporting due diligence, risk management, knowledge management, and operational workflows.
• Design and implement AI agents, multi-agent workflows, and intelligent automation solutions.
• Build conversational assistants, decision-support tools, document processing solutions, and workflow automation capabilities.
• Integrate AI agents with enterprise systems, tools, and business processes.
• Ensure Responsible AI principles, governance controls, and human oversight are incorporated into all solutions.
• Build Retrieval-Augmented Generation (RAG) and knowledge discovery solutions leveraging enterprise data and content.
• Implement semantic search, vector search, knowledge graphs, and enterprise knowledge repositories.
• Enable trusted, secure, and efficient access to organizational knowledge.
• Partner with data engineering teams to ensure data platforms are AI-ready.
• Integrate AI solutions with enterprise data platforms, lakes, warehouses, and semantic models.
• Promote data quality, governance, lineage, security, and compliance practices that support trusted AI.
• Integrate AI capabilities into enterprise applications, workflows, and digital platforms.
• Develop APIs, event-driven integrations, and reusable AI services.
• Collaborate with software, platform, and solution engineering teams to deliver scalable enterprise solutions.
• Support the deployment, monitoring, optimization, and lifecycle management of AI solutions.
• Implement security, privacy, compliance, and AI governance requirements.
• Establish observability, performance monitoring, and operational support processes for AI services.
• Drive adoption of AI capabilities across business and operational teams.
• Provide training, guidance, and best practices for responsible AI use.
• Partner with stakeholders to identify opportunities and maximize business value from AI investments.
• Evaluate emerging AI technologies and identify opportunities to improve business outcomes.
• Lead experimentation, proof-of-concepts, and innovation initiatives.
• Serve as a trusted technical advisor on AI strategy, architecture, and solution development.
• Contribute to AI standards, best practices, and enterprise AI capabilities.

Requirements

• Master’s degree in Computer Science, Software Engineering, Data Science, AI/ML, or a related field, plus at least 8 years of relevant professional experience; or Bachelor’s degree in Computer Science, Software Engineering, Data Science, AI/ML, or a related field, plus at least 10 years of relevant professional experience; or an equivalent combination of education and relevant professional experience.
• 7+ years of experience in software engineering, data engineering, AI solution development, or solution architecture.
• Proven experience delivering AI, automation, or data-driven solutions in enterprise environments.
• Experience working directly with business stakeholders to translate requirements into technology solutions.
• Experience designing and implementing enterprise solutions on AWS, Azure, or Google Cloud.
• Strong understanding of APIs, microservices, event-driven architectures, and system integration patterns.
• Experience building scalable, secure, and production-ready applications and platforms.
• Hands-on experience developing and deploying Generative AI and Agentic AI solutions.
• Practical knowledge of LLMs, prompt engineering, AI agents, orchestration frameworks, and Retrieval-Augmented Generation (RAG).
• Experience delivering AI assistants, knowledge search solutions, document intelligence, workflow automation, or decision-support applications.
• Understanding of Responsible AI principles and AI governance practices.
• Experience building knowledge retrieval and enterprise search solutions using vector search, semantic search, or RAG architectures.
• Familiarity with enterprise content repositories, knowledge graphs, or document management platforms.
• Ability to design solutions that enable trusted and secure access to organizational knowledge.
• Experience working with enterprise data platforms, data lakes, warehouses, and analytics environments.
• Familiarity with Databricks, Microsoft Fabric, Azure Data Lake, AWS data services, or similar platforms.
• Understanding of data governance, metadata management, data quality, and AI-ready data architectures.
• Strong programming skills in Python and modern software engineering practices.
• Experience integrating AI services with enterprise applications, APIs, and digital platforms.
• Familiarity with containerization, serverless technologies, CI/CD pipelines, and cloud-native development approaches.
• Experience with low-code platforms such as OutSystems is an advantage.
• Understanding of MLOps, LLMOps, monitoring, observability, and model lifecycle management.
• Experience implementing security, privacy, and compliance controls within enterprise technology environments.
• Knowledge of AI governance, explainability, auditability, and responsible AI practices.
• Experience delivering AI solutions in financial services, development organizations, risk management, compliance, or regulated environments is preferred.
• Experience with AWS Bedrock, SageMaker, Azure OpenAI, Azure AI Foundry, Microsoft Copilot Studio, or similar enterprise AI platforms is preferred.
• Knowledge of Knowledge Graphs, Semantic Layers, Digital Twins, or Intelligent Automation platforms is preferred.
• Familiarity with Agile, SAFe, Product Operating Models, or enterprise delivery frameworks is preferred.
• Relevant cloud, AI, or architecture certifications are desirable.

Skills

• Software Engineering
• Data Engineering
• AI Solution Architecture
• Solution Architecture leadership
• Enterprise AI Solutions
• Business Requirements Analysis
• AWS cloud platform
• Azure Cloud Infrastructure
• Google Cloud Platform
• APIs
• Microservices Architecture
• Event-Driven Architectures
• System Integration
• Scalable Application Development
• Secure Application Development
• Generative AI
• Agentic AI Solutions
• Large Language Models
• Prompt Engineering
• AI Agents Development
• Orchestration Frameworks
• Retrieval-Augmented Generation
• AI Assistants
• Knowledge Search Solutions
• Document Intelligence
• Workflow Automation
• Decision-Support Applications
• Responsible AI Principles
• AI Governance
• Knowledge Retrieval
• Enterprise search solutions
• Vector Search
• Semantic Search
• Enterprise Content Repositories
• Knowledge Graph Development
• Document Management
• Enterprise Data Platform Management
• Data Lakes
• Data Warehouse
• Data Analytics Environments
• Databricks
• Microsoft Fabric
• Azure Data Lake
• AWS Data Services
• Data Governance
• Metadata Management
• Data Quality Improvement
• AI-Ready Data Architectures
• Python Programming
• Software Engineering Practices
• AI Service Integration
• Containerization Technologies
• Serverless Technologies
• CI/CD Pipelines
• Cloud-native Development
• Low Code No Code Platforms
• MLOps principles
• LLMOps
• Model Lifecycle Management
• Security Controls
• Privacy Controls
• Compliance Controls
• AI Explainability
• AI Auditability
• Financial Services AI
• Risk Management AI
• Compliance AI
• AWS Bedrock service
• SageMaker
• Azure OpenAI
• Azure AI Foundry
• Microsoft Copilot
• Semantic Layers
• Digital Twins
• Intelligent automation
• Agile Methodologies
• SAFe Framework
• Product Operating Models
• Enterprise Delivery Frameworks

Languages

English