Senior IT Officer, Business Solutions I (AI Solutions Officer)

Senior IT Officer, Business Solutions I (AI Solutions Officer) Job #: req34641 Organization: World Bank Sector: Information Technology Grade: GG Term Duration: 2 years 0 months Recruitment Type: Local Recruitment Location: Singapore, Washington, DC, Sofia, Bulgaria Required Language(s): English Preferred Language(s): Closing Date: 11/28/2025 (MM/DD/YYYY) at 11:59pm UTC

Description

Do you want to build a career that is truly worthwhile? Working at the World Bank Group provides a unique opportunity for you to help our clients solve their greatest development challenges. The World Bank Group is one of the largest sources of funding and knowledge for developing countries; a unique global partnership of five institutions dedicated to ending extreme poverty, increasing shared prosperity and promoting sustainable development. With 189 member countries and more than 120 offices worldwide, we work with public and private sector partners, investing in groundbreaking projects and using data, research, and technology to develop solutions to the most urgent global challenges. For more information, visit www.worldbank.org

Background & General Description 

The Information and Technology Solutions (ITS) Vice Presidential Unit supports the World Bank Group’s mission by delivering transformative digital solutions to staff across 150+ global locations. Within ITS, the Operations Effectiveness unit (ITSEF), part of the Operations & Country Engagement Solutions department (ITSOE), leads efforts to enhance operational efficiency and data-driven decision-making in the Bank’s Operations. 

The AI Operations Team within ITSEF focuses on integrating advanced AI/ML technologies into project workflows to improve project task team productivity, automate processes, and strengthen risk management. Key solution areas include Generative AI, Agents, Chatbots, Document Review, and Insights & Classification. 

To advance this vision, ITSEF is hiring a Senior AI Solutions Engineer to design and deploy ethical, secure Agentic AI applications in collaboration with product teams, AI Engineers, data scientists, and business stakeholders. 

To accelerate impact, ITSEF will complement foundation models with custom AI models—including fine-tuned LLMs and Small Language Models (SLMs) to meet business needs, strict enterprise, cost, and latency constraints. The Senior AI Solutions Engineer will lead the full custom model lifecycle (data curation, supervised fine-tuning, preference optimization, safety alignment, evaluation, and ongoing retraining) and will determine when SLMs (e.g., compact 1–13B parameter models) outperform larger models for on-prem, cloud, or edge scenarios across Operations and Knowledge domains. 

Key Responsibilities 

- Custom Model Strategy & Build: Own the strategy to select, customize, and train LLMs/SLMs (e.g., choose between RAG vs. fine-tuning or a hybrid), including dataset development, SFT / LoRA / QLoRA/PEFT, and knowledge distillation to meet cost/latency and dataresidency requirements.  

- SLM First Approaches: Design SLM-centric solutions for constrained environments (limited GPU/CPU, offline/airgapped) with quantization (e.g., INT8/INT4, AWQ, GPTQ) and runtime optimizations (e.g., vLLM/TGI).  

- Training & Alignment Pipelines: Stand up repeatable training/evaluation pipelines (e.g., MLflow/W&B, Azure AI Studio/Prompt Flow, LangSmith) covering data labeling, SFT, DPO/RLHF/Constitutional AI, redteaming, and automatic regression tests for model updates.  

- Model Evaluation & Efficacy: Define business-grounded evals (task-specific metrics, MTBench/arenastyle comparisons, robustness/toxicity/hallucination rates) and A/B experiments with telemetry to drive adoption and confidence.  

- Cost/Performance Governance: Monitor and optimize token spend, throughput, latency, and GPU utilization; set SLOs and budget guardrails for production traffic. Team Leadership: Managing and mentoring a team of AI engineers and data scientists to deliver high-quality AI solutions. 

- Responsible AI & Security: Implement policy, privacy, and safety controls (prompt injection defenses, PII protection, content filtering), and coordinate security/data governance for model cards, datasheets, and audit trails.  

- Agentic AI + Tools: Integrate custom models into agentic frameworks (e.g., LangGraph / CrewAI) and enterprise systems (Kubernetes, microservices, message buses), including tool use, function calling, and retrieval orchestration. 

Selection Criteria

* Advanced degree (Master’s) with 8+ years of experience, or Bachelor’s with 10+ years, in Computer Science, Data Science, Engineering, or related field; or equivalent blend of education and experience. 

* AI Agents: Build AI Agents and applications in generative AI, AI Insights, Document Review, and chatbots. Knowledge of Agentic frameworks like LangGraph / CrewAI is a big plus. 

* Expert in Generative AI, NLP/LLMs, and machine learning, with hands-on experience in building end-to-end Agentic AI solutions using cloud platforms like Azure, AWS, GCP. 

* Experienced in implementing retrieval-augmented generation using Azure AI Search / AWS OpenSearch / GCP, and other vector databases. 

* Strong grasp of diverse database types (SQL, graph, unstructured) with the ability to design efficient data access patterns tailored for AI workloads.  

* Data Pipelines: Hands-on experience building and managing data ingestion and transformation pipelines using Databricks, Azure Data Factory, or equivalent tools. 

* Skilled in Python programming and MLOps, including CI/CD pipelines, model deployment, microservices, and integration with enterprise systems. 

* Natural Language Processing (NLP): Proficiency in NLP techniques, including sentiment analysis, named entity recognition, and machine translation using libraries like spaCy, NLTK, or Hugging Face Transformers. 

* Reinforcement Learning: Experience in developing and implementing reinforcement learning algorithms for decision-making and predictive modeling. 

* AI Infrastructure: Experience with AI infrastructure tools such as Kubernetes, Docker, and cloud platforms like AWS, Azure, or Google Cloud for deploying and scaling AI solutions. 

* Hands-on model customization: Demonstrated experience training and fine-tuning open or proprietary LLMs/SLMs using techniques such as LoRA / QLoRA / PEFT, instructiontuning, and domain adaptation; experience with safety alignment (DPO/RLHF) strongly preferred.  

* SLM production experience: Track record deploying SLMs (e.g., 1–13B class) with quantization, prompt caching, and distillation to meet tight cost/latency envelopes in enterprise settings.  

* Evaluation & safety: Practical expertise defining automated eval suites (task success, factuality, robustness, toxicity) and conducting redteam exercises for enterprise AI.  

* Distributed & efficient training: Experience with FSDP / DeepSpeed / MegatronLM, Ray, or Databricks for efficient training; comfort sizing clusters, storage, and throughput. 

* AI Ethics and Fairness: Understanding of ethical considerations in AI, including bias detection, fairness, and transparency in AI models. 

Desired Qualifications (Strong Plus) 

* Advanced Machine Learning: Expertise in developing and deploying machine learning models using advanced algorithms and techniques. 

* AI Model Interpretability: Skills in making AI models (LLMs & SLMs) interpretable and explainable to non-technical stakeholders. 

* Edge AI: Proficiency in deploying AI models (SLMs) on edge devices for real-time processing and decision-making. 

* Model hosting stacks: Experience operating vLLM/TGI, Hugging Face Inference Endpoints, Azure OpenAI/Azure AI Model Catalog, AWS Bedrock, or Vertex AI; familiarity with tokenizers, KVcache behavior, and batching.  

* Model selection literacy: Practical comparisons across model families (e.g., Llama, Mistral, Gemma, Phi) and criteria for choosing SLM vs. LLM based on accuracy, latency, privacy, and TCO.  

* Observability for LLMs/SLMs: Proficiency with prompt tracing, hallucination detection, guardrails, PII filters, and content moderation stacks. 

* Certifications: Azure AI Engineer Associate, Google ML Engineer, SAFe for DevOps / Architects; AI Ethics certification. 

Required Job Competencies 

- Deliver Results for Clients: Proactively addresses clients’ stated and unstated needs. Adds value by constantly looking for a better way to get more impactful results; sets challenging stretch goals for oneself. Immerses oneself in client experiences and perspectives by asking probing questions to understand unmet needs. 
 
- Collaborate Within Teams and Across Boundaries: Collaborates across boundaries, gives your own perspective, and willingly receives diverse perspectives. Appropriately involves others in decision-making and communicates with key stakeholders. Approaches conflicts as common problems to be solved. Actively seeks and considers diverse ideas and approaches, displaying a sense of mutuality and respect. 

- Model Stewardship & Governance: Establishes repeatable governance around datasets, prompts, and models; maintains model cards, risk registers, and change logs; ensures deployments comply with privacy and responsible AI requirements.  

- Experimental Rigor: Designs hypothesis-driven experiments, sets clear success metrics, and makes data-informed trade-offs (accuracy vs. latency vs. cost) in collaboration with stakeholders. 
 
- Lead and Innovate: Develops innovative solutions. Contributes new insights to understand situations and develops solutions to resolve complex problems. Adapts as circumstances require and manages the impact of one's own behavior on others in the context of WBG’s values and mission. Identifies and pursues innovative approaches to resolve issues. 
 
- Create, Apply, and Share Knowledge: Applies knowledge across WBG to strengthen solutions for internal and/or external clients. Leverages department’s expertise and body of knowledge across WBG to strengthen internal and/or external client solutions. Seeks to learn from more experienced staff to deepen or strengthen their professional knowledge and helps others to learn. 

- Make Smart Decisions: Interprets a wide range of information and pushes to move forward. Seeks diversity of information and inputs, researches possible solutions, and generates recommended options. Identifies and undertakes research and proposes recommendations. Based on risk analysis, they make decisions in a timely manner within their own area of responsibility, considering the interests and concerns of stakeholders. 

- Client Understanding and Advising: Looks at issues from the client’s perspective and advocates for clients within own area. Also urges others to focus on meeting client needs. Works with others across the VPU to define client needs and develop the best approach to meeting client needs.  

Diversity & Inclusion 
We are an equal opportunity and inclusive employer with a dedicated and committed workforce. We do not discriminate based on gender, gender identity, religion, race, ethnicity, sexual orientation, or disability. 

WBG Culture Attributes:

1. Sense of urgency: Anticipate and quickly respond to the needs of internal and external stakeholders.
2. Thoughtful risk-taking: Challenge the status quo and push boundaries to achieve greater impact.
3. Empowerment and accountability: Empower yourself and others to act and hold each other accountable for results.

World Bank Group Core Competencies

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.

We are proud to be an equal opportunity and inclusive employer with a dedicated and committed workforce, and do not discriminate based on gender, gender identity, religion, race, ethnicity, sexual orientation, or disability.

Learn more about working at the World Bank and IFC, including our values and inspiring stories.