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About This Role
AI Engineer
The AI Engineer is responsible for designing, developing, deploying, and maintaining Artificial Intelligence (AI), Machine Learning (ML), and Generative AI solutions that drive innovation, operational efficiency, risk management, and customer experience improvements across Popular.
This position serves as a key technical contributor within Popular’s AI Office and partners closely with Data & Analytics, Enterprise Architecture, Cybersecurity, Infrastructure, Software Engineering, Risk Management, Compliance, Legal, and business units to deliver secure, scalable, and compliant AI solutions. The AI Engineer is responsible for engineering enterprise-grade AI applications, implementing model lifecycle management practices, and ensuring AI solutions align with Responsible AI principles, regulatory requirements, and technology standards applicable to the financial services industry.
The role combines strong software engineering, data science, cloud architecture, and AI platform expertise to accelerate AI adoption while maintaining the highest levels of governance, security, resiliency, and operational excellence.
Key Responsibilities
AI Solution Engineering & Development
- Design, develop, test, deploy, and support AI, Machine Learning, and Generative AI solutions that address strategic business objectives and operational challenges.
- Build scalable AI-enabled applications, APIs, intelligent assistants, virtual agents, copilots, and automation solutions.
- Develop and maintain reusable AI frameworks, engineering standards, and reference architectures to accelerate enterprise adoption.Translate business requirements into technical AI solution designs that meet functional, security, performance,
- and compliance requirements.
- Collaborate with business stakeholders, product owners, and technical teams to deliver production-ready AI capabilities.
Generative AI & Intelligent Automation
- Design and implement Generative AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), semantic search, and enterprise knowledge management capabilities.
- Develop agentic AI workflows that automate and orchestrate business processes while maintaining appropriate governance and human oversight.
- Optimize prompt engineering, model orchestration, grounding strategies, and response quality frameworksEvaluate and integrate emerging AI technologies, platforms, and capabilities that support enterprise innovation objectives.
- Support development of internal AI copilots and digital workforce solutions that improve employee productivity and customer experiences.
AI Platform Engineering & MLOps
- Develop and support AI engineering platforms, model deployment frameworks, and MLOps capabilities.
- Build and maintain CI/CD pipelines, automated testing frameworks, model monitoring, and deployment automation for AI solutions.
- Implement observability, logging, telemetry, and performance monitoring across AI applications and services.
- Support model lifecycle management, version control, retraining, validation, and operational monitoring.
- Ensure AI solutions are scalable, resilient, and aligned with enterprise technology standards.
AI Risk, Governance & Responsible AI
- Ensure AI solutions comply with enterprise policies, cybersecurity standards, model risk management requirements, privacy regulations, and applicable financial industry regulations.
- Support implementation of Responsible AI practices including fairness, transparency, explainability, accountability, and human oversight.
- Participate in AI governance reviews, model validation processes, architecture reviews, and risk assessments.
- Maintain technical documentation, model artifacts, deployment records, inventories, and audit evidence required for regulatory and internal oversight.
- Collaborate with Risk Management, Compliance, Legal, Information Security, and Internal Audit teams throughout the AI lifecycle.
Innovation & Continuous Improvement
- Research emerging AI, machine learning, and cloud technologies to identify opportunities for business value creation.
- Participate in proof-of-concepts, pilot initiatives, and innovation programs sponsored by the AI Office.
- Provide technical guidance and mentorship to engineering and technology teams regarding AI engineering best practices.
- Contributes to the establishment of enterprise AI engineering standards, development methodologies, and operating procedures.
Required Qualifications
Education
- Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Information Systems, Data Science, Artificial Intelligence, Mathematics, Statistics, or a related technical field.
- Master's degree in Artificial Intelligence, Data Science, Computer Science, Engineering, or related discipline preferred.
Experience
- 5+ years of experience in software engineering, machine learning engineering, AI engineering, data engineering, or related technology disciplines.
- Experience designing, developing, and deploying production-grade AI or machine learning solutions.
- Experience working with cloud platforms and enterprise technology environments.
- Experience implementing CI/CD, DevOps, MLOps, and software engineering best practices.
- Experience operating within highly regulated industries such as banking, financial services, insurance, healthcare, or government is preferred.
- Experience collaborating across technical and business teams in a matrixed environment.
Technical Skills
- Strong proficiency in Python and software engineering best practices.
- Experience with AI and machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, LangChain, Semantic Kernel, or similar technologies.
- Experience with Large Language Models (LLMs), Generative AI architectures, prompting techniques, retrieval systems, and agent frameworks.
- Experience developing APIs, microservices, and cloud-native applications.
- Knowledge of Azure AI services, Azure Machine Learning, Azure OpenAI, containerization technologies, and cloud-native architectures.
- Familiarity with vector databases, knowledge retrieval platforms, model deployment frameworks, and AI monitoring tools.
- Understanding of information security, data privacy, access management, and cybersecurity principles.
Core Competencies
- AI and Machine Learning Engineering
- Software Engineering Excellence
- Cloud and Platform Engineering
- Problem Solving and Analytical Thinking
- Innovation and Technology Curiosity
- Responsible AI and Risk Awareness
- Collaboration and Influencing Skills
- Business Acumen and Customer Focus
- Continuous Learning and Adaptability
- Results Orientation and Accountability
Preferred Certifications and Tool Proficiency
- AWS AI/ML or Generative AI certification, with hands-on proficiency in Amazon Bedrock, SageMaker, RAG, agentic AI, and production GenAI solutions.
- Demonstrated proficiency with AI engineering tools such as Claude Code, Kiro, Amazon Q Developer, MCP, AWS Lambda, API Gateway, CloudFormation/CDK, and CI/CD pipelines.
- Azure AI Engineer, Azure Developer, Databricks Machine Learning, or equivalent certification supporting multi-cloud AI engineering and MLOps.Security certification such as CISSP, cloud security, or equivalent credential supporting secure AI development in regulated environments.
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. Mid-level AI roles across all categories have a median of $193,200.
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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