Responsibilities
- Lead the design and deployment of AI agent systems to address complex challenges in generative AI, document analysis, chatbots, and insight generation within operational and knowledge management contexts.
- Supervise and guide a team of AI engineers and data scientists to develop robust, scalable AI solutions.
- Maintain awareness of emerging AI technologies and integrate cutting-edge methods into ongoing projects.
- Partner with multidisciplinary teams to embed AI capabilities into current platforms and business processes.
- Define performance indicators and oversee model effectiveness to ensure alignment with strategic goals.
- Produce detailed records and progress reports to inform stakeholders of project developments and results.
Requirements
- Master’s degree with at least 8 years of relevant experience, or Bachelor’s with 10+ years, in Computer Science, Data Science, Engineering, or related discipline; or a comparable combination of education and professional background.
- Demonstrated ability to develop AI agents and applications in generative AI, AI-driven insights, document processing, and conversational interfaces; familiarity with LangGraph or CrewAI frameworks is highly valued.
- In-depth knowledge of generative AI, natural language processing, large language models, and machine learning, with practical experience delivering full lifecycle AI solutions on cloud platforms such as Azure, AWS, or GCP.
- Proven track record implementing retrieval-augmented generation systems using Azure AI Search, AWS OpenSearch, GCP, or similar vector databases.
- Solid understanding of various data storage systems including SQL, graph, and unstructured databases, with expertise in designing optimized data access for AI applications.
- Hands-on experience constructing and maintaining data pipelines for ingestion and transformation using tools like Databricks or Azure Data Factory.
- Proficient in Python and MLOps practices, including CI/CD workflows, model deployment, microservices architecture, and integration with enterprise infrastructure.
- Strong skills in natural language processing techniques such as sentiment analysis, named entity recognition, and machine translation using libraries including spaCy, NLTK, or Hugging Face Transformers.
- Experience applying reinforcement learning methods to decision-making systems and predictive modeling tasks.
- Familiarity with AI deployment infrastructure, including Kubernetes, Docker, and cloud environments like AWS, Azure, or Google Cloud.
- Ability to enhance AI model efficiency through optimization methods such as pruning, quantization, and knowledge distillation.
- Knowledge of protecting AI systems from adversarial threats and ensuring data confidentiality and integrity.
- Understanding of ethical AI principles, including identifying bias, ensuring fairness, and promoting transparency in model design and outputs.
Nice to Have
- Advanced proficiency in designing and fielding machine learning models using sophisticated algorithms.
- Capability to explain complex AI model behaviors to non-technical audiences, enhancing interpretability of large and small language models.
- Experience deploying lightweight AI models on edge devices for low-latency inference and real-time decision support.
- Hold certifications such as Azure AI Engineer Associate, Google ML Engineer, SAFe for DevOps/Architects, or AI Ethics credentials.
Benefits
- Comprehensive benefits package including retirement planning, health and life insurance, disability coverage, paid time off, parental leave, and accommodations for employees with disabilities.
Benefits
The World Bank Group offers comprehensive benefits, including a retirement plan; medical, life and disability insurance; and paid leave, including parental leave, as well as reasonable accommodations for individuals with disabilities.
Other
- English
- 2 years 0 months
