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About This Role
Join Zepz: Breaking Down Borders, Together
At Zepz, we're all about breaking down barriers for our customers and our teammates. Our purpose is deeply personal, we provide a lifeline and deliver opportunities to cross-border communities that need it most through cutting edge finance and tech. Zepz is the power behind WorldRemit and Sendwave, driven by the mission to celebrate the incredible impact migrants have, both at home and abroad. We're not just moving money; we're building a world that truly recognizes and supports global connections. Who You'll Be Joining We look for mission-driven builders who thrive in a fast-paced environment connected to a true purpose. We’re an always developing team of experts that enjoy problem solving and bring clarity to tough challenges. At Zepz, we are Relentless Learners, always seeking feedback, and Responsible Owners, treating company resources like our own. We depend on Transparent Communicators who keep the team aligned through open, honest talk.
Our Core Commitments — What We Live By
Our vibrant and truly diverse culture is built on three core commitments, that guide how we work and interact:
- Integrity: We always do the right thing. It’s simple, but it’s the foundation of everything we build for our customers and each other.
- Outcomes: We work for our customers. Their success and prosperity are the results we focus on delivering.
- Velocity: We are fast! We maintain high energy levels and make smart decisions quickly, keeping us nimble and ahead of the curve.
Perks of Joining Our Team
We believe in empowering colleagues because we genuinely trust our team. Our culture is founded on this high trust, which naturally encourages the high ownership that drives us to meet our shared high expectations and deliver high performance. While we have all the tools to work remotely, we value the purpose and benefits of in-person connections so we expect our teams to come together for team off-sites and connects. Alongside a great flexible working environment, think unlimited annual leave , great healthcare benefits, and employee discounts. We want you to thrive and focus entirely on making your biggest impact! In turn, we expect you to bring high ownership and commitment to your work. This is a place where we value trust and high performance, and we'll provide the environment and support needed for you to excel
About the role
This is a high-visibility, high-accountability role — outcomes are reported to C-level and the board, and the role owns both the data strategy and the model performance behind systems that directly protect revenue and customer trust. The mandate in one line: own what data is generated and how it is generated and consumed across the Identity and Financial Crime domains. Almost every consequential decision Zepz makes about a transaction — is this person who they say they are, is this payment safe, is this account compromised — is a decision made on data. Identity is our single largest producer of that data. Financial Crime is our single largest consumer of it. Both sit in the same engineering organization, and today no one owns the path between them. The result is a permanent tax on everything downstream: problems discovered late, by the people least able to fix them at source. This role exists to remove that tax. You will lead a team of 25+ Data Scientists, Machine Learning Engineers and Analytics Engineers across the Identity and Financial Crime domains
What you will own* Own the definition, generation, quality and consumption of data across the Identity and Financial Crime domains — what gets produced, what it means, what guarantees come with it, and who is accountable when those guarantees are not met.
- Make data quality an engineering discipline rather than a downstream cleanup exercise: producer/consumer contracts, freshness and integrity monitoring, lineage, a named owner for every critical dataset, and a real path from a detected anomaly to a fix at source. You'll be measured on whether downstream teams stop discovering data problems by accident.
- Own the models and analytics behind how Zepz detects fraud and financial crime — feature engineering, model development, deployment, monitoring and retraining. You own model performance in production, not just model development.
- Lead a mixed organization of Data Scientists, Machine Learning Engineers and Analytics Engineers. These are three different crafts with three different career paths, and leading them well means understanding all three rather than favoring the one you came from.
- Partner as a peer with the Engineering Manager who owns the fraud detection platform and reports directly to the VP Engineering. Your models run on their platform: you own model outcomes, they own platform outcomes, and you jointly own the seam between them.
- Partner with our central data function, which owns core data infrastructure and platform tooling. You are a demanding customer and a collaborator, not a replacement — you build domain data products and quality practice on top of what they provide, and feed real requirements back to them.
- Set standards with Identity engineering teams who do not report to you. Much of your impact depends on changing how data is produced upstream, which is influence work: making the cost of poor data visible with evidence rather than assertion, and making it easy for those teams to do the right thing.
- Create high-performing teams with a bias toward shipping without sacrificing reliability or trust in the numbers. Use technical and data debt as a tool to move faster, while ensuring your teams have the space to build it right once they know what the right thing to build is.
- Hire, retain, and level up superlative talent to ensure our team remains world-class as we continue to scale and launch into new markets.
- Roll up your sleeves when models degrade or data breaks. You'll need to discuss technical solutions, review work in depth, and help execute strategy with your leads.
In your first few months, you will:* Establish an honest, evidenced baseline of data quality across the identity-to-financial-crime path — what breaks, how often, who finds out, and what it costs in downstream hours and model degradation. Bring the number, not the anecdote.
- Agree a written ownership model with the central data team and the Identity engineering leads: who owns which datasets, what the contracts are, and what happens when one is broken.
- Instrument the critical path, so data issues are detected by monitoring rather than by an analyst noticing that something looks odd.
- Get to know the models — how they perform, how they degrade, how retraining actually works today, and where risk is concentrated.
- Assess the team: identify the gaps, the flight risks and the people ready for more, and start hiring against a plan rather than against a vacancy.
- Promote a culture of ownership, accountability, and shipping.
What you bring to the table* You have a track record of leading a data organization of comparable size (roughly 15–25 people) and of supporting the professional development of managers and senior-level ICs.
- Track record of leading mixed technical teams — Data Scientists, ML Engineers, Analytics Engineers — with genuine credibility across all three crafts.
- Demonstrable ownership of data quality at scale: contracts, observability, lineage, semantic consistency — with evidence of the downstream impact, not just the tooling you installed.
- Fluency with modern lakehouse and analytics engineering practice: Databricks (or equivalent), dbt-style transformation, feature store concepts and real-time feature serving, and the discipline of treating datasets as products with owners and SLAs.
- Experience with production ML in a real-time context: model monitoring, drift detection, and retraining pipelines.
- Proven experience with AWS at scale.
- Direct experience leading FinCrime/Fraud or Identity engineering or data science teams (not just adjacent risk/security).
- The ability to get outcomes from teams that don't report to you. We'd rather hear about a time you changed a producer team's behavior than a time you escalated.
- At least 8+ years of technical experience in a hybrid IC/management role.
- You can attract and retain top talent and lead teams with diverse skill sets.
- You work well with Engineering and Product leaders and are comfortable building effective relationships with folks outside of the Prod-Eng spheres.
- You have exceptional judgment; we're scaling quickly — you'll be required to make good tradeoffs.
FinCrime & Identity Domain
- Bring prior experience leading FinCrime/Fraud or Identity teams — understanding of typologies (card fraud, ATO, first-party fraud, synthetic identity, money mule networks), the regulatory context, and how detection strategy trades off against customer experience
- Understand identity verification, KYC and onboarding data as a data domain, not just as a product surface
- Partner with Risk, Compliance, and Legal on regulatory obligations tied to fraud and identity controls
- Own incident response leadership when major fraud events, model failures, or data integrity failures occur
Bonus points* Have worked at a scaling startup previously.
- Have experience with data mesh or domain-oriented data ownership models in practice, not just in theory.
- Have experience working with stablecoin and cryptocurrencies.
- Have experience working with AI in a production environment.
- Have worked in environments with k8s / gRPC / Spring Boot / Java / Python.
AI Literacy & Tools
As Zepz, we're building AI into how we work. We're on a journey to making AI utilisation streamline what we do. "For this position we are looking for:
- Proficient depth of knowledge around automation, AI and how those can be applied in your area of work — from accelerating analysis and insight generation, to improving how we design and deliver processes and communications.
- Practical experience using AI tools in a professional context — whether to connect information, draft content, build frameworks, or create insights from data. We want people who actively use AI to increase productivity, not those who are waiting to be told to.
- Strong AI literacy — an understanding of what AI assistants can and cannot do, how to prompt effectively, and how to critically evaluate AI-generated output before using it.
- Awareness of the ethical considerations and responsible use of AI in the workplace, particularly in the context of sensitive data, customer experience, and compliance.
- Prior experience using Claude (Anthropic) is a nice-to-have but not essential — what matters most is that you are genuinely engaged with AI as a tool, comfortable experimenting, and eager to share what works with your wider team
If you want to join us in our journey to help break barriers in financial access and improve lives globally, there's no better place or time to join.
Our global team of 800+ people is spread across six continents. We aspire to hire the best mix of people from former Olympians to YouTube influencers and we speak over twenty languages. This incredible diversity isn't a bonus; it's the engine that lets us serve the world.
Ready to Apply? Let’s Go.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At ZEPZ TECHNOLOGY SERVICES LIMITED, 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 Required
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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
ZEPZ TECHNOLOGY SERVICES LIMITED AI Hiring
ZEPZ TECHNOLOGY SERVICES LIMITED 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 $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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