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About This Role
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BBVA is a global company with more than 160 years of history that operates in more than 25 countries where we serve more than 80 million customers. We are more than 121,000 professionals working in multidisciplinary teams with profiles as diverse as financiers, legal experts, data scientists, developers, engineers and designers.
About the job:
We are looking for someone to join the team and help us accelerate the adoption of Artificial Intelligence across Compliance and Non-Financial Risk at BBVA .
As AI Transformation Lead – Compliance & Non-Financial Risk , you will work alongside Compliance and Risk teams to discover how Generative AI, agentic AI, advanced analytics and automation can strengthen the way we identify, assess, monitor and manage compliance and non-financial risks.
Your mission will be to connect business, Compliance and risk management needs with what AI can now make possible , helping teams move from opportunity identification through prototyping, controlled implementation, adoption and measurable value.
We are looking for someone who can connect Compliance and Risk challenges, AI capabilities and transformation , bringing together the right people to turn ideas into practical solutions.
In your day-to-day
- You will work closely with teams across Compliance and Non-Financial Risk to understand their processes, challenges and control needs, and identify concrete opportunities where AI can improve risk identification, analysis, monitoring, controls and decision-making .
- You will design, build and test AI-enabled solutions together with subject-matter experts, leveraging BBVA's available AI capabilities and platforms.
- You will assess the value and feasibility of different opportunities, identifying the information, data, applications, integrations, governance requirements and other dependencies needed to make them successful.
- You will bring together teams across Compliance, Non-Financial Risk, Technology, Data, Model Risk, Information Security, Privacy, Legal and other disciplines to move promising ideas from use case prototype controlled implementation measurable value .
- You will develop prototypes, test them with users and iterate based on their feedback until they become genuinely useful solutions.
- You will help teams adopt these capabilities and integrate them effectively into their existing processes, controls and ways of working .
- You will identify opportunities to create reusable AI capabilities that can address similar needs across different teams rather than building isolated point solutions.
- You will translate developments in AI into practical opportunities for Compliance and Non-Financial Risk, helping teams understand both what the technology can do and where its limitations lie .
- You will share good practices, ways of working and lessons learned, helping strengthen AI capabilities across the Compliance and Risk community.
- You will promote the responsible, secure and controlled use of AI , aligned with BBVA's governance, information-protection, risk-management and control frameworks.
The opportunities are broad and will continue to evolve. They could include areas such as regulatory intelligence and regulatory change, Compliance and Non-Financial Risk assessments, AML and Sanctions analysis, monitoring and testing, AI-powered knowledge and research, management information and executive reporting, KRIs and KPIs, issue and emerging-risk analysis, policy and control-gap analysis, and automation of information-intensive processes .
They could also include new agentic workflows that augment the capabilities of Compliance and Non-Financial Risk teams .
These are examples rather than a predefined roadmap. An important part of the role will be discovering new opportunities together with the teams and determining where AI can create the greatest value while maintaining appropriate controls.
What are we looking for?
This is a highly practical and cross-functional role.
We are looking for someone who enjoys understanding complex regulatory and control problems, challenging how things are done today, experimenting, building solutions and helping teams adopt new ways of working .
You do not need to be a software engineer or a career Compliance specialist.
You do need to be interested in Compliance, risk management and the control environment of a financial institution , and comfortable learning new areas of the domain quickly.
If you are passionate about AI, follow its evolution closely and naturally find yourself thinking “How could we apply this across Compliance and Non-Financial Risk at BBVA?” , this role could be for you.
You should be able to listen to business or control problems, understand the underlying need and translate it into a practical solution. This requires moving comfortably between regulatory and business context, AI and technology, data, processes and controls , while working with both technical and non-technical stakeholders.
You should also be comfortable operating where not all the answers are known in advance. Curiosity, autonomy, pragmatism and the ability to make progress through ambiguity will be essential.
We are looking for a strong team player who enjoys working across organizational boundaries, sharing knowledge and learning from others. Collaboration will be fundamental to making the transformation successful.
In return, you will have the opportunity to work at the intersection of AI, Compliance and transformation at BBVA , experimenting with emerging capabilities and turning them into solutions with visible impact.
Responsible AI
AI-enabled solutions must be useful while meeting the governance, control and accountability standards expected of a financial institution .
You will work with the relevant teams to ensure that AI capabilities are designed and implemented with appropriate consideration for information protection, privacy, security, accuracy, testing, explainability, human oversight, traceability, model risk and regulatory accountability .
This includes understanding where AI can appropriately support and augment professional judgment , where human review and oversight remain necessary, and how solutions can be implemented in a controlled and sustainable way.
The objective is to help BBVA achieve better risk identification, stronger and more effective controls, greater analytical coverage, better information and insights, faster and simpler processes, and meaningful productivity improvements across Compliance and Non-Financial Risk .
If your instinct when looking at a complex Compliance or Risk process is “There has to be a smarter way to do this,” this may be the role for you.
Experience and knowledge
- Bachelor’s Degree required
- 5+ years experience in Compliance, Risk, strategy, transformation, business, technology, data, analytics, automation or process improvement .
- Hands-on experience using Generative AI to solve real business problems , including identifying use cases, developing prototypes, testing solutions with users and iterating based on feedback.
- Strong knowledge of the evolving AI landscape, particularly OpenAI and Google's enterprise AI ecosystems and their agentic capabilities, including ChatGPT Work, Gemini Enterprise and Google Flows .
- Good understanding of prompting, AI assistants and agents, workflow automation, knowledge-based AI solutions , and how AI can connect with enterprise information and applications.
- Ability to assess opportunities from both a value and feasibility perspective , identifying relevant technology, data, security, governance and operational dependencies.
- Experience working across different areas and bringing together stakeholders with different expertise and priorities.
- Knowledge of BBVA, its organization, culture and ways of working is a plus.
Personal capabilities
- Genuine passion for AI and curiosity about its rapidly evolving possibilities.
- Interest in Compliance, risk management and how AI can strengthen control activities .
- A builder mindset : you enjoy experimenting, testing ideas and turning them into useful solutions.
- Strong listening skills and the ability to understand what is really needed behind a Compliance, Risk or control problem.
- Intellectual curiosity and the ability to learn unfamiliar regulatory and risk domains quickly.
- Ability to move comfortably between strategic thinking and hands-on problem solving .
- Excellent communication skills and the ability to build trust with Compliance and Risk professionals, senior leaders and technical teams.
- A collaborative mindset and a natural tendency to connect people, share knowledge and help others succeed .
- Autonomy, initiative and comfort making progress when not everything is defined.
- Pragmatism and good judgment about where AI can create value — and where it cannot .
- Sensitivity to the regulatory, operational, reputational, data and ethical implications of AI .
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
With respect to this position in our New York Office, the expected base salary ranges from $120,000 to $145,000. It is not typical for offers to be made at or near the top of the range. Salary offers are based on a wide range of factors including relevant skills, training, experience, education, and, where applicable, certifications obtained. Market and organizational factors are also considered. In addition to salary and a generous employee benefits package, successful candidates are eligible to receive a discretionary bonus.
*Employment eligibility to work with BBVA in the U.S. is required as the company will not pursue visa sponsorship for these positions
Legal requirements
It is not typical for offers to be made at or near the top of the range. Salary offers are based on a wide range of factors including relevant skills, training, experience, education, and, where applicable, certifications obtained. Market and organizational factors are also considered. In addition to salary and a generous employee benefits package, successful candidates are eligible to receive a discretionary bonus.
Pay Transparency Policy Statement
The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor's legal duty to furnish information (41 C.F.R. 60-1.35 (c)).
Individuals with Disabilities
BBVA USA, BBVA Securities Inc., and BBVA S.A. New York Branch invite all interested and qualified applicants to apply for employment opportunities. If you are a U.S.-based job seeker with a disability who is unable to use our online tools to search and apply for jobs, please contact us by emailing: [email protected] or by calling toll-free (in the U.S.) 1-844-664-9275. Please indicate the specific type of assistance needed*.
- The disability access telephone line and email address are reserved solely for job seekers with disabilities requesting accessibility assistance or an accommodation. Please do not call about the status of your job application if you do not require accessibility assistance or an accommodation. Messages left for other purposes, such as following up on an application or non-disability related or technical issues, will not receive a response.
EEO Statement
BBVA USA, BBVA Securities Inc., and BBVA S.A. New York Branch have a firm and unwavering policy to provide equal employment opportunity without regard to age, citizenship, color, disability, ethnic origin, gender, gender identity and expression, marital status, nationality, national origin, race, religion, sexual orientation, genetic predisposition, protected veteran status, or any other status or classification protected by federal, state or local law. This policy includes all job groups, classifications and organizational units. With regard to employment, this policy extends to applicants and covers our recruiting, hiring, promotion, transfer, demotion, discipline, termination, benefits, compensation and training practices as well as social and recreational activities.
View the " EEO is the Law " & " View the EEO is the Law Supplement Poster " poster. BBVA USA, BBVA Securities, Inc., and BBVA NY are equal opportunity and affirmative action employer.
Salary Context
This $120K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →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 BBVA, 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. This role's midpoint ($132K) sits 38% below the category median. Disclosed range: $120K to $145K.
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.
BBVA AI Hiring
BBVA has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, New York, NY. Compensation range: $145K - $145K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national 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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