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About This Role
Description
DISTINGUISHED ENGINEER, AI TOOLING \& SECURITY
Why This Role Matters
This role will help define and secure the future of AI at Citizens. The Distinguished Engineer, AI Tooling \& Security will shape enterprise engineering practices, accelerate responsible AI adoption, and ensure that AI driven capabilities are developed, deployed, and operated securely at scale while enabling innovation and business growth.
Role Summary
Citizens is seeking a highly accomplished Distinguished Engineer, AI Tooling \& Security to drive the design, engineering, and security of AI powered platforms and applications across the enterprise. This role combines deep software engineering expertise with advanced security knowledge to ensure AI solutions are scalable, resilient, secure, and aligned with business objectives.
As a Distinguished Engineer, you will serve as a hands on technical leader and peer mentor, partnering with engineering, architecture, security, data, and business teams to shape the future of AI adoption at Citizens. You will lead the development of innovative solutions, establish engineering standards, influence strategic technology decisions, and advance secure AI capabilities across the organization.
Key Responsibilities
- Lead the design, development, and implementation of innovative software solutions, platforms, and tools that support enterprise AI initiatives.
- Collaborate with engineering and architecture teams to define scalable, secure, and maintainable technology solutions aligned with enterprise standards.
- Build modern, cloud native applications and reusable components that accelerate business outcomes and technology innovation.
- Incorporate scalability, reliability, performance, observability, and maintainability into distributed systems and platform designs.
- Champion engineering excellence through code quality, automation, testing, and continuous improvement practices.
- Serve as a senior technical leader and mentor, fostering a culture of innovation, accountability, and continuous learning.
- Build and operationalize security controls that protect AI applications, models, agents, prompts, and outputs from misuse and abuse.
- Secure the full AI and machine learning lifecycle, including data ingestion, model development, training, deployment, monitoring, and runtime operations.
- Design safeguards against emerging AI threats, including prompt injection, data poisoning, model inversion, adversarial attacks, and unauthorized data exposure.
- Strengthen identity, authentication, authorization, and access management controls for AI systems, APIs, services, and cloud environments.
- Secure integrations between AI platforms and enterprise applications, databases, APIs, and SaaS solutions to prevent unauthorized access and data exfiltration.
- Implement encryption, tokenization, data masking, and privacy preserving controls to protect sensitive information used by AI systems.
- Develop monitoring, logging, detection, and alerting capabilities to identify anomalous AI behaviors, policy violations, and security threats.
- Harden cloud platforms, containers, orchestration environments, and infrastructure supporting AI workloads.
- Evaluate security risks associated with third party AI platforms and services, ensuring appropriate governance and control frameworks.
- Integrate automated security validation, adversarial testing, and model robustness assessments into engineering and deployment pipelines.
- Lead technical investigations and response activities related to AI security incidents, model misuse, and data exposure events.
- Collaborate with architecture, governance, risk, compliance, and engineering teams to align AI security practices with regulatory requirements and business objectives.
Required Qualifications
- 10\+ years of software engineering, platform engineering, security engineering, or data engineering experience.
- Demonstrated success leading large scale engineering initiatives and influencing technical direction across multiple teams.
- Strong understanding of AI, machine learning, generative AI, and agentic AI architectures and implementation patterns.
- Experience developing secure, cloud based applications and services.
- Strong programming experience in Python and proficiency in at least one additional modern programming language.
- Experience working with large scale data platforms and analytical workloads.
- Hands on experience with AWS cloud technologies and securing sensitive workloads.
- Strong Linux and scripting experience, including Bash.
- Experience building and maintaining CI/CD pipelines using Jenkins, CircleCI, GitHub Actions, or similar technologies.
- Deep understanding of application security, cloud security, identity and access management, API security, and secure software development practices.
- Knowledge of AI security principles, model governance, threat modeling, and secure deployment patterns.
- Strong understanding of data structures, algorithms, and distributed systems.
- Excellent communication, collaboration, and stakeholder management skills.
- Proven ability to mentor and develop engineers while driving technical excellence.
Preferred Qualifications
- Experience securing enterprise AI, machine learning, or generative AI platforms.
- Experience within financial services, banking, fintech, or other highly regulated industries.
- Cloud certifications such as AWS Solutions Architect, AWS Security Specialty, Azure Solutions Architect, or equivalent.
- Experience with container platforms, Kubernetes, infrastructure as code, and platform engineering practices.
- Familiarity with emerging AI governance, model risk management, and regulatory frameworks.
Education
- Required: Bachelor's degree or equivalent combination of education and experience.
- Preferred: Bachelor's or Master's degree in Computer Science, Software Engineering, Computer Engineering, Data Science, Cybersecurity, or a related technical discipline.
Compensation
The salary range for this position is $175,000 to $250,000 per year, plus an opportunity to earn additional incentive earnings. Actual pay is based on various factors including, but not limited to, the budget, work location, and relevant skills and experience.
Benefits
Comprehensive benefits include medical, dental, and vision coverage, retirement plans, parental leave, flexible work arrangements, education reimbursement, wellness programs, and generous paid time off exceeding local requirements.
https://jobs.citizensbank.com/benefits.
*Some job boards have started using jobseeker\-reported data to estimate salary ranges for roles. If you apply and qualify for this role, a recruiter will discuss accurate pay guidance.*
Equal Employment Opportunity
Citizens, its parent, subsidiaries, and related companies (Citizens) provide equal employment and advancement opportunities to all colleagues and applicants for employment without regard to age, ancestry, color, citizenship, physical or mental disability, perceived disability or history or record of a disability, ethnicity, gender, gender identity or expression, genetic information, genetic characteristic, marital or domestic partner status, victim of domestic violence, family status/parenthood, medical condition, military or veteran status, national origin, pregnancy/childbirth/lactation, colleague’s or a dependent’s reproductive health decision making, race, religion, sex, sexual orientation, or any other category protected by federal, state and/or local laws. At Citizens, we are committed to fostering an inclusive culture that enables all colleagues to bring their best selves to work every day and everyone is expected to be treated with respect and professionalism. Employment decisions are based solely on merit, qualifications, performance and capability.
Background Check
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Any offer of employment is conditioned upon the candidate successfully passing a background check, which may include initial credit, motor vehicle record, public record, prior employment verification, and criminal background checks. Results of the background check are individually reviewed based upon legal requirements imposed by our regulators and with consideration of the nature and gravity of the background history and the job offered. Any offer of employment will include further information.
Benefits
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We offer competitive pay, comprehensive medical, dental and vision coverage, retirement benefits, maternity/paternity leave, flexible work arrangements, education reimbursement, wellness programs and more.
Awards We've Received
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Dave Thomas Foundation’s Best Adoption\-Friendly Workplace
Glassdoor
Best Place to Work in Consulting, Finance \& Insurance
Human Rights Campaign Corporate Equality Index 100 Award
Newsweek America's Most Charitable Company
Disability:IN Best Places To Disability Inclusion
The Banker's
US Bank of the Year
Salary Context
This $175K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Citizens, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $175K to $250K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Citizens AI Hiring
Citizens has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Westwood, MA, US, Johnston, RI, US. Compensation range: $250K - $250K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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