AI Engineer

AI Engineer
1. Background and Context
The AI Engineer will play a pivotal role in designing developing and deploying artificial intelligence solutions that enhance operational efficiency automate decision-making and support strategic initiatives for the environmental and social specialists in the World Bank Group. This role is central to the VPUs digital transformation efforts and will contribute to the development of scalable ethical and innovative AI systems.

2. Qualifications and Experience Education - Bachelors or Masters degree in Computer Science Data Science Engineering or related field. Experience - Minimum 3 years of experience in AI/ML model development and deployment.
- Experience with MLOps tools (e.g. MLflow) Docker and cloud platforms (AWS Azure GCP).
- Proven track record in implementing LLMs RAG NLP model development and GenAI solutions. Technical Skills

• Skilled in Azure AI/Google Vertex Search Vector Databases fine-tuning the RAG NLP model development API Management (facilitates access to different sources of data)

• Proficiency in Python TensorFlow PyTorch and NLP frameworks.
• Expertise deep learning computer vision and large language models.
• Familiarity with REST APIs NoSQL and RDBMS.

Soft Skills - Strong analytical and problem-solving abilities.
- Excellent communication and teamwork skills.
- Strategic thinking and innovation mindset. 3. Certifications (Preferred) - Microsoft Certified: Azure AI Engineer Associate
- Google Machine Learning Engineer
- SAFe Agile Software Engineer (ASE)
- Certification in AI Ethics 4. Objectives of the Assignment - Develop and implement AI models and algorithms tailored to business needs.
- Integrate AI solutions into existing systems and workflows.
- Ensure ethical compliance and data privacy in all AI initiatives.
- Support user adoption through training and documentation.
- Support existing AI solutions by refinement troubleshooting and reconfiguration 5. Scope of Work and Responsibilities AI Solution Development - Collaborate with cross-functional teams to identify AI opportunities.
- Train validate and optimize machine learning models.
- Translate business requirements to technical specifications. AI Solution Implementation - Develop code deploy AI models and into production environments and conduct ongoing model training
- Monitor performance and troubleshoot issues and engage in fine-tuning the solutions to improve accuracy
- Ensure compliance with ethical standards and data governance policies. User Training and Adoption - Conduct training sessions for stakeholders on AI tools.
- Develop user guides and technical documentation. Data Analysis and Research - Collect preprocess and engineer large datasets for machine learning and AI applications.
- Recommend and Implement Data Cleaning and Preparation
- Analyze and use structured and unstructured data (including geospatial data) to extract features and actionable insights.
- Monitor data quality detect bias and manage model/data drift in production environments.
- Research emerging AI technologies and recommend improvements. Governance Strategy Support and Maintenance - Advise WBG Staff on AI strategy and policy implications
- Contribute to the teams AI roadmap and innovation agenda.
- Provide continuous support and contribute towards maintenance and future enhancements. 4. DeliverablesMP1 - Work on Proof of Concepts to study the technical feasibility of AI Use Cases