Interested in this AI/ML Engineer role at United Nations Development Programme?
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Background
The Word Meteorological Organization (WMO) is implementing a project to develop and deploy Artificial Intelligence (AI)\-powered tools to automate the collection, extraction, structuring, and visualization of information related to hydrometeorological (Hydromet) projects.
The project aims to strengthen transparency, coordination, and accountability in Hydromet investments by developing an AI\-driven data extraction system and an associated Partner Coordination Mechanism (PCM) Dashboard. The solution will automate the extraction of project information from donor websites project documents, standardize information across multiple funding sources, and support evidence\-based decision\-making through interactive visualizations.
To support the successful implementation of this initiative, WMO seeks an AI Product Owner \& Quality Assurance Consultant to provide operational oversight, quality assurance, stakeholder coordination, and validation support throughout the project lifecycle.
Duties and Responsibilities
Under the supervision of the Project Coordinator, the consultant will be responsible for the following:
1\. Product ownerships and requirements management;
- Support the translation of business requirements into detailed functional and technical specifications.
- Maintain and track project requirements, enhancement requests, issues, and corrective actions, and support prioritization of activities in consultation with project stakeholders.
- Coordinate regular implementation meetings with AI tool and dashboard developer and relevant stakeholders.
- Monitor project progress and support risk identification and mitigation.
2\. Quality assurance and validation;
- Develop testing and validation in line with state\-of\-the\-art computer science methods.
- Develop testing data sets.
- Conduct quality assurance and acceptance testing.
- Review extraction accuracy and confidence scores.
- Coordinate user testing.
3\. Technical coordination and sustainability;
- Liaise with IT focal point to ensure maintainability, documentation, architecture, hosting, security and handover requirements and incorporated from the start.
- Review technical architecture, documentation, and deployment approached from an operational sustainability perspective.
- Ensure maintainability, scalability, security, and operational requirements are adequately addressed.
- Support preparation and execution of knowledge transfer and handover activities.
- Verify that technical documentation, user documentation, and maintenance procedures meet WMO operational requirements.
4\. Stakeholder coordination and reporting;
- Maintain issue and risk logs.
- Facilitate communication between WMO stakeholders, AI tool and dashboard developers, the Technical Focal Point, and the IT Focal Point.
- Prepare implementation progress report and recommendations.
- Escalate technical and operational risks as required.
- Support project governance and decision\-making processes through evidence\-based recommendations.
Deliverables at the end of the contract:
- Inception Report including detailed workplan, implementation methodology, risk assessment, and quality assurance framework in line with state\-of\-the\-art computer science methods.
- Review of product requirements and acceptance criteria at each phase of the project.
- AI Validation and Testing Frameworks including (i) testing methodology, (ii) validation protocols, (iii) performance indicators, and (iv) accuracy benchmarks.
- Validation report including (i) testing results, (ii) accuracy assessments, (iii) gap analysis, and (iv) recommendations for improvement.
Support activities related to (i) Validation framework, (ii) Test datasets, (iii) Pilot Quality Assurance report, (iv) Accuracy assessment reports, (v) Donor template mapping catalogue, (vi) User acceptance testing reports, (vi) Final quality assurance report and (vii) Knowledge transfer package.
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Required Skills and Experience
Education
- Advanced university degree in (Master's degree or equivalent) in Computer Science, Data Science, Information Systems, Engineering, Digital Transformation, Business Analytics, or a related field.
A first\-level university degree in International Relations, Economy, Business and Administration, or related field; combined with additional demonstrated qualifying experience in programming, machine learning and artificial intelligence for similar projects may be accepted in lieu of an advanced degree.
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Experience
- Evidence of working knowledge in generative AI applications.
- AI/NLP experience (Large Language Models, Retrieval systems, Information extraction, document processing).
Evidence of working experience in the translation of business needs into automated systems.
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Other Requirements
- Strong understanding of AI lifecycle management, validation methodologies, and quality assurance principles.
- Knowledge of software development methodologies, including Agile and iterative delivery approaches.
- Ability to translate business requirements into technical specifications.
- Strong analytical, organizational, and problem\-solving skills.
- Excellent stakeholder management and communication skills.
- Ability to work independently and manage multiple priorities in an international and multicultural environment.
- Familiarity with climate finance, hydrometeorological services, or international development projects.
Desirable:
- Experience supporting development assistance projects.
- Experience with AI governance, responsible AI principles, and data quality management frameworks.
- Previous experience working with UN agencies.
Languages
Excellent knowledge of English (both oral and written) is required.
(Note: The official languages of the Organization are Arabic, Chinese, English, French, Russian and Spanish).
Payband: B (will be published only the range corresponding to the payband).
Duration: 90 days over a period of 9 months
Additional Information :
The consultant will work under the overall supervision of the Project Coordinator and WMO Focal Point for the project: Ana Laura Zuanazzi ([email protected])
Applications:
Applications should be made online through the WMO e\-recruitment system.
Do not send your application via multiple routes. WMO no longer accepts applications via post or email. Only applicants for whom WMO has a further interest will be contacted. Shortlisted candidates may be required to sit a written test and/or an interview.
Sexual harassment, exploitation, and abuse of authority
WMO does not tolerate harassment, sexual harassment, exploitation, discrimination and abuse of authority. All selected candidates, therefore, undergo relevant checks and are expected to adhere to the respective standards and principles.
Scam alert
WMO does not charge a processing fee at any stage of its recruitment, selection, and hiring processes (i.e., application stage, interview stage, validation stage, or appointment and training). WMO will not ask for applicants’ bank account information.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At United Nations Development Programme, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
United Nations Development Programme AI Hiring
United Nations Development Programme has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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