Interested in this AI/ML Engineer role at Citizens Business Bank?
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About This Role
SUMMARY Reporting directly to the CIO, the AI Productivity Specialist will lead the Bank’s enterprise adoption of Microsoft Copilot for the enterprise; this includes Copilot Chat (free), the licensed version of Copilot, and Copilot Studio. This role will design and deliver training in partnership with HR, curate role-based use cases and prompts, and manage the Bank’s internally hosted AI Innovation Hub. The ideal candidate blends strong Microsoft 365 expertise with change management, communications, and compliance awareness to drive secure, measurable productivity gains across all divisions and job roles.
ESSENTIAL DUTIES and RESPONSIBILITIES* Independently designs and executes enterprise-wide rollout plans for Copilot Chat, Microsoft 365 Copilot, and Copilot Studio. Defines enablement roadmaps, pilot frameworks, readiness benchmarks, and scaling strategies, applies sound judgement to tailor approaches for diverse business units.
- Exercises discretion in developing role-specific learning paths and instructional materials (e.g., decks, guides, videos) that align with organizational goals. Evaluates training effectiveness and refines content based on feedback and evolving needs.
- Serves as the principal architect and curator of the Bank’s intranet-based Copilot resource center. Determines content priorities, structure, and updates to ensure relevance, accuracy, and strategic alignment with enterprise AI initiatives.
- Partners with stakeholders across divisions to identify, assess, and document high-value Copilot use cases. Applied independent judgement to vet and publish Copilot Agents and prompt libraries that reflect operational controls and workflow optimization.
- Provides expert level coaching on prompt engineering, ethical AI usage, and secure deployment practices. Serves as the primary point of contact for Copilot-related inquiries, offering tailored guidance that reflects nuanced understanding of business priorities.
- Independently plans and executes enterprise communication campaigns (e.g., newsletters, demos, town halls) that promote Copilot adoption. Recognizes champions and fosters a culture of responsible innovation through targeted messaging and engagement strategies.
- Complies with and stays abreast of all policies and procedures, federal and state laws applicable to the job.
- Provide, present, and promote The Citizens Experience to all external and internal customers.
- Other duties as assigned. SUPERVISORY RESPONSIBILITIES
This position has no supervisory responsibilities. QUALIFICATIONS To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skills, and/or abilities required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. EDUCATION and/or EXPERIENCE Bachelor’s degree in computer science or a related discipline from a four-year college or technical school or 1-2 years of job-related experience. LANGUAGE SKILLS Ability to read, analyze, and interpret general business periodicals, professional journals, technical procedures, or governmental regulations. Ability to write reports, business correspondence, and procedure manuals. Ability to effectively present information and respond to questions from groups of managers, clients, customers, and the general public. MATHEMATICAL SKILLS Ability to apply simple math concepts to add, subtract multiply, and divide whole numbers using a PC or calculator. REASONING ABILITY Ability to define GenAI use cases to address inefficiencies in processes, collect data, establish facts, and draw valid conclusions. Ability to interpret an extensive variety of technical instructions in mathematical or diagram form and deal with several abstract and concrete variables. COMPUTER SKILLS
- Knowledge of Artificial Intelligence and Generative Artificial Intelligence
- Experience working with GenAI solutions (Copilot, ChatGpt, Gemini, etc.)
- Experience with Microsoft 365 Copilot and Copilot Studio preferred
- Microsoft 365 product stack knowledge a plus CERTIFICATES, LICENSES, REGISTRATIONS
None required. OTHER SKILLS and ABILITIES
- Thorough understanding of Generative Artificial Intelligence
- Strong oral and written communication skills evidenced by a background in preparing comprehensive documentation incorporating complex information.
- Highly detail oriented with strong organizational skills.
- Ability to work with third party vendors, in-house technology partners, or peers within the bank
- Ability and desire to work in a fast-paced environment.
- Proven ability to work independently and in a team environment with minimal supervision.
- The incumbent should be self-motivated with strong decision making and communication skills.
- Ability to plan, revise and execute project work plans as appropriate to meet changing needs and requirements.
- Strong problem-solving skills across both process and technical domains. PHYSICAL DEMANDS
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is frequently required to sit. The employee is occasionally required to stand; walk; use hands to finger, handle, or feel; reach with hands and arms; climb or balance; stoop, kneel, crouch, or crawl; talk or hear and taste or smell. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception and ability to adjust focus. WORK ENVIRONMENT The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
The noise level in the work environment is usually moderate.
Salary Range:$73,770.00 To $103,278.00 Annually
Exact compensation may vary based on skills, experience and/or education, and location.
Salary Context
This $73K-$103K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Citizens Business Bank, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($88K) sits 58% below the category median. Disclosed range: $73K to $103K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Citizens Business Bank AI Hiring
Citizens Business Bank has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rancho Cucamonga, CA, US. Compensation range: $103K - $103K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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