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About This Role
About DEUNA \uD83E\uDDE1
DEUNA is a rapidly growing startup revolutionizing global commerce with ATHIA, our AI-powered orchestration and payments platform that helps large enterprises boost approval rates, reduce costs, and unlock new revenue. Built by the team behind DEUNA—the fastest-growing Commerce OS in Latin America—ATHIA combines payment intelligence, checkout optimization, and data orchestration in one powerful solution.
With deep integrations across 300+ PSPs and alternative payment methods, and over 20% of Mexico’s digital economy running through our platform, we simplify global payments through a single integration and centralized reconciliation. We are a rapidly growing startup expanding into the U.S. to meet the urgent needs of large retailers, marketplaces, airlines, and QSRs. Join us to shape the future of payments! \uD83D\uDE80
Visit https://www.deuna.com/ to learn more about us!
About the Role
DEUNA is looking for an Engineering Manager to lead our AI/ML engineering team — the group building the intelligent systems behind payment routing optimization, authorization rate improvement, and our AI-powered digital workforce products. You will manage a team of engineers (currently ~7-8) spanning ML, backend, and platform work, while staying close enough to the technical details to guide architecture decisions, unblock your team, and represent the group's roadmap to product and leadership.
This is a hands-on management role: you won't be writing production code day-to-day, but you need enough depth in ML systems, backend services, and AI/LLM workflows to make good calls on architecture, review technical approaches, and coach engineers through hard problems. You'll own delivery for the team — scoping, costing, and sequencing initiatives — while building a low-tech-debt, high-quality engineering culture.
Who You Are
- 8+ years in software engineering, including 2-3+ years in a people management role leading engineers.
- You have significant experience building and shipping backend and/or ML systems at scale, ideally with exposure to fintech, payments, or another regulated, latency-sensitive domain.
- You've managed engineers before, hiring, developing, and retaining a team, and you know how to balance people development with delivery pressure.
- You have hands-on familiarity with modern AI/ML systems: model training and serving, LLM-powered workflows (agents, RAG, orchestration), or similar — enough to have a real technical conversation with your team and challenge their thinking when needed.
- Practical exposure to LLM-based systems in production (agents, RAG, or AI workflow orchestration).
- Payments, fintech, or another regulated-industry background is a strong plus, but not required.
- You communicate clearly and proactively, both with your team and with cross-functional partners in product and operations.
- You're comfortable in a fast-moving startup environment — priorities shift, and you can re-scope and re-communicate without losing the team's trust.
- You're driven by self-improvement and push the people around you to grow as well.
What You'll Do
Team & People Leadership
- Manage and grow a team of AI/ML and platform engineers, including hiring, performance development, and career growth.
- Coach engineers through technical design decisions, code and architecture reviews, and hard trade-offs.
- Set the engineering bar for the team: code standards, testing strategy, and CI/CD practices.
- Drive accurate costing and delivery estimates for initiatives, and keep the team accountable to commitments.
Technical Ownership
- Guide architecture for ML model lifecycle work (training, evaluation, monitoring, retraining) and LLM-powered workflows (agent orchestration, RAG pipelines, vector DB integrations).
- Oversee inference services supporting live payment routing, ensuring they meet strict latency and reliability requirements.
- Ensure the team's AWS infrastructure, CI/CD, and observability practices (dashboards, tracing, on-call runbooks) meet a high bar.
- Apply sound judgment on PCI-DSS and data-handling requirements across anything touching payment data.
Cross-functional Collaboration
- Translate product vision into an executable technical roadmap with clear timelines and trade-offs.
- Partner directly with product, operations, and modeling leadership to keep feedback loops short and priorities aligned.
- Represent the AI/ML engineering team's progress and blockers to leadership.
Tech Stack (for context, not all required)
- Backend / Platform: Go, Python, gRPC & REST APIs, event streaming, distributed systems
- Cloud & Infra: AWS (ECS/EKS, Terraform, RDS/Aurora, S3), hybrid/on-prem deployment
- AI / ML Stack: PyTorch/TensorFlow, XGBoost/scikit-learn, MLflow/W&B, model monitoring
- LLMs & Agents: LangGraph/LangChain, RAG, vector DBs, prompt engineering, LLM evaluation
- Payments Domain: PCI-DSS awareness, tokenization patterns, PSP integrations, routing/auth rate optimization
- Observability: Prometheus/Grafana, OpenTelemetry, structured logging, on-call runbooks
What will you find when you join DEUNA?
- A multicultural team distributed throughout LATAM
- Dynamism, agility and constant innovation
- Being part of a high-impact solution for an entire region
- The best tools and technology to operate
- Being part of the startup culture.
- We are in full expansion!
Benefits: Vacations and additional PTO \uD83C\uDFDD️ Remote work from anywhere \uD83D\uDCBB Economic support for health insurance, internet and cell phone line\uD83D\uDCF1\uD83C\uDF10 We all own DEUNA, we offer stock options \uD83D\uDCB8 Learning and development platform \uD83D\uDCDA Multidisciplinary, diverse and dynamic team \uD83E\uDDE1 Growth and career path \uD83D\uDE80
Be part of a dynamic team that's creating the next generation payments platform.
DEUNA is proud to be an equal opportunity employer. We value and celebrate diversity and inclusion in our workplace. All employment decisions at DEUNA are made without discrimination based on nationality, race, ethnicity, religion, gender, sexual orientation, gender identity or expression, marital status, color, disability, or any other characteristic protected by law.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 deuna, 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. Mid-level AI roles across all categories have a median of $193,200.
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.
deuna AI Hiring
deuna 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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