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### General information
Career area
Deposits Products \& Marketing
Work Location(s)
601 S. Tryon Street, NC
Remote?
No
Ref \#
22525
Posted Date
07\-15\-26
Working time
Full time
### Ally and Your Career
Ally Financial only succeeds when its people do \- and that’s more than some cliché people put on job postings. We live this stuff! We see our people as, well, people \- with interests, families, friends, dreams, and causes that are all important to them. Our focus is on the health and safety of our teammates as well as work\-life balance and diversity and inclusion. From generous benefits to a variety of employee resource groups, we strive to build paths that encourage employees to stretch themselves professionally. We want to help you grow, develop, and learn new things. You’re constantly evolving, so shouldn’t your opportunities be, too? Work Schedule: Ally designates roles as (1\) fully on\-site, (2\) hybrid, or (3\) fully remote. Hybrid roles are generally expected to be in the office a certain number of days per week as indicated by your manager. Your hiring manager will discuss this role's specific work requirements with you during the hiring process. All work requirements are subject to change at any time based on leader discretion and/or business need.
### The Opportunity
In office role, hybrid schedule, Ally work location is Charlotte NC
The Consumer Bank is accelerating the adoption of Artificial Intelligence to improve customer experiences, create operating leverage, strengthen risk management, and unlock new sources of business value. We are seeking a Director, AI Strategy and Use Case Management to serve as the day\-to\-day leader for the Consumer Bank AI portfolio.
This role will partner closely with Consumer Bank leadership, Product, Technology, Data, Analytics, Operations, Risk, Compliance, and other partners to convert strategic priorities into a disciplined portfolio of AI\-enabled use cases. The Director will manage intake, prioritization, operating routines, delivery oversight, governance, and value realization across the portfolio.
The successful candidate will be a strong operator who can bring structure to ambiguity, coordinate across complex stakeholder groups, and keep high\-value AI initiatives moving from concept through experimentation, implementation, and scale. The role will contribute to AI strategy development, but its primary focus is execution, portfolio management, and measurable business outcomes.
### The Work Itself
AI Portfolio and Use Case Management* Manage the Consumer Bank AI use case portfolio and maintain a clear view of active, pending, and emerging opportunities.
- Partner with business leaders to identify AI opportunities aligned with customer needs, operational priorities, risk objectives, and product goals.
- Establish and run a standardized intake, evaluation, sizing, and prioritization process for AI use cases.
- Evaluate opportunities based on business value, customer impact, feasibility, risk, data readiness, technology readiness, and implementation complexity.
- Maintain a multi\-quarter roadmap of prioritized AI initiatives, dependencies, decisions, and expected outcomes.
- Prepare materials for portfolio reviews, leadership discussions, funding decisions, and prioritization tradeoffs.
Use Case Execution and Delivery Oversight* Coordinate across Product, Technology, Data Science, Analytics, Operations, and business teams to move use cases from idea through experimentation, pilot, deployment, and scale.
- Drive clarity on initiative objectives, business owners, success metrics, scope, milestones, dependencies, and decision points.
- Monitor progress across the AI portfolio, identify delivery risks, and escalate issues requiring leadership attention.
- Help teams align on reusable capabilities, common delivery patterns, and implementation playbooks where appropriate.
- Support the transition of successful experiments into durable operating models, production capabilities, and business routines.
Governance, Measurement, and Value Realization* Establish operating routines that provide transparency into portfolio health, delivery status, risks, decisions, and realized business value.
- Develop portfolio reporting, scorecards, dashboards, and executive\-ready updates for Consumer Bank leadership.
- Define success metrics and value realization approaches for major AI initiatives, including adoption, efficiency, revenue, risk, customer, and operational outcomes as appropriate.
- Partner with Risk, Compliance, Legal, Security, Model Governance, and Technology partners to ensure responsible AI expectations are incorporated into the delivery lifecycle.
- Maintain accountability for benefits tracking and post\-implementation learning across the AI portfolio.
Strategy Enablement* Support Consumer Bank leadership in refining AI priorities by providing portfolio insights, use case analysis, market context, and execution considerations.
- Translate senior leadership direction into practical roadmaps, operating plans, and measurable delivery plans.
- Identify patterns across use cases that may influence future strategic priorities, capability investments, governance needs, or organizational readiness.
- Help ensure AI work remains focused on business outcomes rather than technology experimentation for its own sake.
Organizational Enablement and Change Leadership* Build strong working relationships across business, product, technology, operations, analytics, data, and risk teams.
- Promote a culture of experimentation, learning, responsible innovation, and outcome\-based delivery.
- Support communication, stakeholder alignment, and change management for AI\-enabled capabilities.
- Help increase organizational understanding of how AI can be applied responsibly to solve priority business problems.
### The Skills You Bring
### Minimum Qualifications
- 9\+ years of relevant experience or equivalent combination of education and experience
- High school Diploma or GED equivalent
### Preferred Qualifications
- Bachelor's degree in related field strongly preferred, advanced degree preferred.
- 7\+ years of experience in strategy and operations, product management, portfolio management, program management, consulting, business transformation, digital transformation, technology management, analytics, or a related discipline.
- 7\+ years experience in banking, financial services, fintech, payments, or another regulated industry.
- 7\+ years leadership experience preferred.
- Experience working with technology, data science, product, operations, risk, and business teams in a matrixed environment.
- Demonstrated experience leading complex cross\-functional initiatives from concept through execution.
- Strong business acumen with the ability to connect use cases to measurable customer, financial, operational, or risk outcomes.
- Experience building business cases, prioritization frameworks, roadmaps, executive reporting, and portfolio governance routines.
- Practical understanding of AI, machine learning, generative AI, automation, advanced analytics, or digital transformation concepts.
- Exceptional written and verbal communication skills, including the ability to create executive\-ready materials.
- Strong stakeholder management skills and ability to influence without direct authority.
- Ability to operate in ambiguity, create structure, and drive accountability across multiple teams.
\#LI\-Hybrid
### How We'll Have Your Back
Ally's compensation program offers market\-competitive base pay and pay\-for\-performance incentives (bonuses) based on achieving personal and company goals. Our Total Rewards program includes industry\-leading compensation and benefits plus additional incentives that are designed to meet your needs and those of your family so you can get the most out of your career and your life, including:* Time Away: Program starts at 20 paid time off days in addition to 11 paid holidays and 8 hours of volunteer time off yearly (time off days are prorated based on start date and program varies based on full or part\-time status and management level).
- Planning for the Future: plan for the near and long term with an industry\-leading 401K retirement savings plan with matching and company contributions, student loan pay downs and 529 educational save up assistance programs, tuition reimbursement, employee stock purchase plan, and financial learning center and financial coach access.
- Supporting your Health \& Well\-being: flexible health and insurance options including medical, dental and vision, employee, spouse and child life insurance, short\- and long\-term disability, pre\-tax Health Savings Account with employer contributions, Healthcare FSA, critical illness, accident \& hospital indemnity insurance, and a total well\-being program that helps you and your family stay on track physically, socially, emotionally, and financially.
- Building a Family: adoption, surrogacy and fertility assistance as well as paid parental and caregiver leave, Dependent Day Care FSA back\-up child and adult/elder care days and childcare discounts.
- Work\-Life Integration: other benefits including Mentally Fit Employee Assistance Program, subsidized and discounted Weight Watchers® program and other employee discount programs.
- Other compensations: depending on the role for which you are considered, you may be eligible for travel allowances, relocation assistance, a signing bonus and/or equity.
- To view more detailed information about Ally’s Total Rewards, please visit this link: https://www.ally.com/content/dam/pdf/corporate/ally\-total\-rewards\-snapshot.pdf
Who We Are:
Ally Financial is a customer\-centric, leading digital financial services company with passionate customer service and innovative financial solutions. We are relentlessly focused on "Doing it Right" and being a trusted financial\-services provider to our consumer, commercial, and corporate customers. For more information, visit www.ally.com.
Ally is an equal opportunity employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to age, race, color, sex, religion, national origin, disability, sexual orientation, gender identity or expression, pregnancy status, marital status, military or veteran status, genetic disposition or any other reason protected by law.
We are committed to working with and providing reasonable accommodation to applicants with physical or mental disabilities. For accommodation requests, email us at [email protected]. Ally will not discriminate against any qualified individual who is capable of performing the essential functions of the job with or without reasonable accommodation.
Base Pay Range: $125000 \- $190000 USD
An individual's position in the range is determined by the specific role, the scope and responsibilities of the role, work experience, education, certification(s), training, and additional qualifications. We review internal pay, the competitive market, and business environment prior to extending an offer.
Incentive Compensation: This position is eligible to participate in our annual incentive plan.
Salary Context
This $125K-$190K range is below 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 Ally Financial, 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($157K) sits 28% below the category median. Disclosed range: $125K to $190K.
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.
Ally Financial AI Hiring
Ally Financial has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlotte, NC, US. Compensation range: $190K - $190K.
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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