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About This Role
General Description
The AI Platform Engineering Lead is responsible for building and leading the Bank's AI Platform Engineering function, enabling the secure, scalable, and responsible adoption of Artificial Intelligence across the enterprise technology ecosystem.
This role combines technical leadership, people leadership, and hands-on platform engineering expertise. The selected candidate will establish and lead a team of AI Platform Engineers responsible for designing, implementing, and supporting enterprise AI capabilities that enhance software delivery, developer productivity, automation, observability, and platform operations.
The AI Platform Engineering Lead will partner with Engineering, Architecture, Cybersecurity, Product, and Technology leaders to deliver innovative AI-enabled solutions while ensuring compliance with security, governance, risk, and regulatory requirements. The ideal candidate is a strong engineering leader with experience building cloud-native platforms, enabling modern software delivery practices, and implementing enterprise AI technologies in complex and regulated environments.
Essential Duties and Responsibilities
Leadership & Team Development
- Build, develop, and lead a high-performing AI Platform Engineering team.
- Establish team objectives, delivery standards, performance expectations, and career development plans.
- Provide technical coaching, mentorship, and guidance to engineers supporting AI platform initiatives.
- Foster a culture of collaboration, innovation, accountability, continuous learning, and operational excellence.
- Partner with senior technology leaders to define and execute the vision, roadmap, and strategy for AI platform services.
AI Platform Engineering
- Design, implement, and support enterprise AI platform capabilities that accelerate adoption of Artificial Intelligence across the organization.
- Lead the integration, configuration, and operational management of AI platforms and services, including large language model (LLM) technologies and managed AI services.
- Develop reusable platform components, automation frameworks, and engineering standards that enable secure and scalable AI adoption.
- Implement and maintain AI-enabled developer productivity solutions and engineering automation capabilities.
- Ensure platform reliability, scalability, availability, performance, and operational excellence.
- Collaborate with cross-functional technology teams to deliver enterprise-ready AI capabilities aligned with business objectives.
Cloud, Automation & Platform Architecture
- Design and implement cloud-native solutions leveraging modern engineering and platform practices.
- Develop and maintain Infrastructure as Code (IaC) solutions using Terraform, CloudFormation, or similar technologies.
- Integrate AI capabilities into software development, DevSecOps, testing, automation, and cloud platform workflows.
- Contribute to reference architectures, engineering standards, and reusable technology frameworks.
- Drive adoption of modern software engineering practices and platform automation strategies.
Security, Governance & Risk Management
- Ensure AI platform capabilities comply with enterprise security, governance, risk, privacy, and regulatory requirements.
- Partner with Cybersecurity teams to implement secure deployment practices, monitoring controls, access management, and risk mitigation strategies.
- Support threat modeling, vulnerability management, security reviews, and audit readiness efforts.
- Maintain documentation, technical standards, operational procedures, and governance controls supporting AI platform services.
- Promote responsible AI practices and secure software development methodologies.
FinOps, Observability & Operational Excellence
- Establish monitoring, observability, reporting, and operational metrics for AI platform services.
- Develop dashboards and reporting capabilities to measure platform health, adoption, utilization, reliability, and performance.
- Partner with technology and finance stakeholders to support cloud financial management and optimization initiatives.
- Identify opportunities to improve operational efficiency, resource utilization, cost management, and platform effectiveness.
- Drive continuous improvement through data-driven decision making and operational insights.
Education
Bachelor's Degree in Computer Science, Computer Engineering, Information Systems, Software Engineering, or a related field required.
Master's Degree in Computer Science, Artificial Intelligence, Engineering, Information Technology, Business Administration, or a related discipline is preferred.
Experience
- Eight (8) years or more of experience in Software Engineering, Platform Engineering, Cloud Engineering, DevOps, Site Reliability Engineering, or related technology disciplines.
- Two (2) years or more of experience working with Artificial Intelligence platforms, Machine Learning infrastructure, Generative AI technologies, Large Language Models (LLMs), or related solutions.
- Experience designing, deploying, and supporting cloud-native solutions within AWS, Microsoft Azure, or Google Cloud Platform environments.
- Experience leading technical teams and driving complex cross-functional technology initiatives.
- Demonstrated success delivering technology solutions in regulated or highly controlled environments.
- Experience implementing Infrastructure as Code (IaC), CI/CD pipelines, cloud automation frameworks, and modern software delivery practices.
- Experience working with secure software development methodologies and DevSecOps practices.
Knowledge, Skills and Abilities
- Strong understanding of AI platforms, Generative AI technologies, Large Language Models (LLMs), and enterprise AI adoption strategies.
- Knowledge of AWS Bedrock, Azure OpenAI, Vertex AI, or comparable enterprise AI platforms.
- Strong understanding of cloud-native architectures, APIs, distributed systems, automation frameworks, and platform engineering practices.
- Experience implementing modern DevOps, DevSecOps, and CI/CD methodologies.
- Familiarity with AI orchestration frameworks, agent-based systems, and emerging AI platform technologies.
- Knowledge of Infrastructure as Code (Terraform, CloudFormation, or similar tools).
- Understanding of cybersecurity principles, identity and access management, secure design, and risk management practices.
- Experience with observability, monitoring, logging, analytics, and operational reporting solutions.
- Knowledge of cloud financial management and technology cost optimization practices.
- Excellent communication, leadership, collaboration, problem-solving, and stakeholder management skills.
- Ability to balance strategic planning with hands-on execution in a fast-paced environment.
Certifications
Preferred certifications include:
- AWS Certified Solutions Architect
- AWS Certified Developer
- AWS Certified Security Specialty
- FinOps Certified Practitioner
- Certified Kubernetes Administrator (CKA)
- CISSP or equivalent cybersecurity certification
Important: The candidate must provide evidence of academic preparation or courses related to the job posting, if necessary.
Our hybrid work model benefit applies to certain positions and is subject to changes based on the organizational needs.
Applicants must be authorized to work for any employer in the United States. This position is not open to applicants who need visa sponsorship or transfer of visa sponsorship at this time.
ABOUT US
Popular is Puerto Rico’s leading financial institution and have been evolving since it was founded over a century ago. From a small bank it has developed into a large corporation that offer a wide variety of services and financial solutions to our customers, with presence in the United States, the Caribbean and Latin America.
As employees, we are dedicated to making our customers dreams come true by offering financial solutions in each stage of their life. Our extensive trajectory demonstrates the resiliency and determination of our employees to innovate, reach for the right solutions and strongly support the communities we serve; therefore, we value their diverse skills, experiences and backgrounds.
We reaffirm our commitment to always offer essential financial services and solutions for our customers and communities, including during emergency situations and/or natural disasters. Popular’s employees are considered essential workers, whose role is critical in the continuity of these important services even under such circumstances. By applying to this position, you acknowledge that Popular may require your services during and immediately after any such events.
If you have a disability or need more information about requesting an accommodation, please contact us at [email protected]. This email inbox is monitored for such types of requests only***. All information you provide will be kept confidential and will be used only to the extent required to provide needed exemptions or reasonable accommodations. Any other correspondence will not receive a response.*
Are you ready for a rewarding career?
Popular is an Equal Opportunity Employer, including Disability/Vets
Learn more about us at www.popular.com and keep updated with our latest job postings at www.jobs.popular.com*.*
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Popular, Inc., 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Popular, Inc. AI Hiring
Popular, Inc. has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Juan, PR, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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