GenAI Solution Architect

GenAI solution Architect
Washinton D.C - Onsite

1. BACKGROUND

The International Finance Corporation (IFC) is accelerating the adoption of Artificial Intelligence (AI), cloud-native solutions, and emerging technologies to improve operational efficiency, enhance business capabilities, and drive innovation across the organization. As part of this effort, IFC seeks an experienced Solution Architect to evaluate emerging AI solutions, rapidly develop proof-of-concepts (POCs), validate business value, and guide successful transition of solutions from experimentation to enterprise-scale production environments.The consultant will work closely with business stakeholders, enterprise architects, product managers, developers, security teams, and operations teams to identify, assess, and implement innovative solutions leveraging Microsoft Azure, AI technologies, and modern software engineering practices.

. The primary objectives of this assignment are to:
• Identify and evaluate emerging AI and cloud technologies relevant to IFC business needs.
• Design and implement rapid proof-of-concepts to validate business and technical feasibility.
• Define enterprise architecture and production readiness requirements for selected solutions.
• Establish scalable, secure, and maintainable Azure-based solution architectures.
• Accelerate software delivery through the adoption of AI-assisted development practices.
• Provide architectural leadership throughout the solution lifecycle from ideation to production deployment.
 
2. SCOPE OF WORK
 
• Conduct market research on emerging AI platforms, foundation models, AI agents, orchestration frameworks, developer tools, and enterprise AI products.
• Analyze vendor offerings, market trends, product maturity, security considerations, and implementation models.
• Assess solutions against IFC business requirements, enterprise architecture standards, and regulatory requirements.
• Design end-to-end solution architectures leveraging Microsoft Azure services.
• Define architecture patterns for AI, cloud-native applications, integrations, APIs, data platforms, and automation solutions.
• Ensure alignment with enterprise architecture, security, governance, and operational standards.
• Develop architecture artifacts, design documents, and implementation roadmaps.
• Integrate GenAI agents into the existing back-end codebase, APIs, and data pipelines.
Ensure production readiness by addressing error handling, retries, idempotency, logging, and monitoring.
• Participate in the full software development lifecycle, from design and implementation to testing, deployment, and production support.
• Collaborate with business stakeholders to refine requirements and solution vision.
• Support project teams through architecture reviews, technical assessments, and implementation planning.
• Participate in architecture governance forums and design reviews.
• Mentor development teams on modern cloud and AI architecture practices.
• Participate in code reviews and contribute to shared engineering standards and best practices.
• Actively participate in Agile ceremonies, including sprint planning, daily stand-ups, and retrospectives.

3. SKILLS / EXPERIENCE REQUIRED

• Bachelor’s degree or Masters degree in computer science, software engineering, or a related field.
• Minimum 10 years of experience in software engineering and solution architecture.
• Minimum 5 years of experience designing enterprise solutions on Microsoft Azure.
• Demonstrated experience evaluating emerging technologies and conducting technical market assessments.
• Proven track record of delivering proof-of-concepts and scaling them into production environments.
• Experience leading enterprise architecture and cloud transformation initiatives.
• Experience working in complex enterprise organizations and cross-functional teams.
• Strong grasp of production engineering concepts such as error handling, retry, idempotency, observability, and fault tolerance.
• Proficiency in building APIs using FastAPI or similar frameworks.
• Experience with CI/CD pipelines and Git-based version control workflows.
• Ability to work autonomously in ambiguous problem spaces and make sound engineering trade-offs.