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About This Role
Senior Lead Information Security Consultant (AI)
At Capital One, you will help consult on initiatives, programs, and projects to raise their game in Information Security. You are pragmatic and practical in your understanding of risk and security, but also willing to know when to pull in experts and escalate. You collaborate and innovate with other teams within Capital One to push the envelope. You are comfortable with Cloud Service technologies like Storage Services, Security \& Access Control Management, Container Services, and API Implementation and Management. You are familiar with various Cloud computing models to include IaaS, PaaS, and SaaS along with their architectural differences. Security is essential to what we do here, from protecting our customers to our associates.
You will play a leading role in delivering product security advisory services for a fast moving project within a line of business portfolio, working closely with other professionals as required.You have the ability to lead complex problem solving in partnership with multiple stakeholders in a fast\-paced environment, driving results with critical impact. You will work with the other Information security consultants, business, technology and risk partners to achieve time sensitive goals and objectives in a secure manner with a heavy forward lean on modern software and technology architectures.
Responsibilities:
- Act as an Information Security point of contact for a business function within the Card line of business
- Coordinate and execute proactive Information Security consulting to the business and technology teams covering Infrastructure Security, Resiliency, Data Security, Network Architecture and Design, and User Access Management
- Serve as an expert in Capital One’s Information Security capabilities, solutions, policies, procedures and standards
- Leverage strong technical acumen and be security SME reviewing architecture, providing risk mitigation solutions and driving overall risk management.
- Partner closely with engineers, product managers, and other cross\-functional partners to help break down complexity and organizational silos to problem solve.
- Influence customers to leverage security capabilities and solutions to shift and integrate security to the left in the development processes
- Escalate and manage cyber security risk
- Provide ad hoc support on special Information Security hot topics for the business
- Provide regular updates to executive leadership with your line of business on the overall Information Security health and risk environment
About You:
- You have a desire to work in a very fast moving, forward leaning, and modern computing environment
- You have experience in securing large\-scale e\-commerce platforms, with deep understanding of payments systems, customer data protection across high transaction environments ensuring protection of user data across internal and partner ecosystems.
- You have a deep passion for Securing modern computing platforms
- You have a strong desire to continually learn about new technologies
- You possess strong conceptual thinking and communication skills
- You are able to work well under minimal supervision
- You are a demonstrated leader with team\-oriented interpersonal skills and the ability to interface effectively with a broad range of people and roles, including upper management, IT leaders, and technology vendors
- You maintain calmness and clarity of thought under pressure and ability to maintain confidentiality
- You have a deep understanding of strategic business objectives and the ability to drive results toward those objectives
Basic Qualifications:
- High School Diploma, GED or equivalent certification
- At least 6 years of experience working in cybersecurity or information technology
- At least 5 years of experience providing guidance and oversight of cybersecurity concepts
- At least 5 years of experience performing security risk assessments and security architecture reviews
- At least 5 years of experience with architecture, software design, networking, or cloud infrastructure
- At least 4 years of experience with cloud security engineering
- At least 1 year of experience in implementing Artificial Intelligence (AI) in cybersecurity
Preferred Qualifications:
- Bachelor’s Degree
- 2\+ years of experience evaluating AI technologies and integrations for security, privacy, and compliance risks, with knowledge of common AI attack vectors, model vulnerabilities, prompt injection, data leakage, model abuse, and supply chain risks
- 7\+ years of experience Application Security, Threat Modeling, Penetration Testing, Vulnerability Management
- 5\+ years of experience in securing a public cloud environment (e.g. AWS, GCP, Azure)
- 2\+ years experience in e\-commerce industry
- 2\+ years of experience building software utilizing public cloud (e.g. AWS, GCP, Azure)
- 1\+ years of experience with securing Container services
- 1\+ years of experience with Splunk\-Fu and Enterprise Monitoring experience
- 1\+ years of experience in a Financial services industry experience
- 1\+ years of experience with Offensive or Defensive Security techniques
- AWS Certified Solutions Architect or Certified Information Systems Security Professional (CISSP) certification
At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F\-1 OPT, F\-1 STEM OPT, F\-1 CPT, J\-1, TN, or another type of work authorization).
The minimum and maximum full\-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part\-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
McLean, VA: $229,900 \- $262,400 for Sr Manager, Cyber Technical
Plano, TX: $209,000 \- $238,500 for Sr Manager, Cyber Technical
Richmond, VA: $209,000 \- $238,500 for Sr Manager, Cyber Technical
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well\-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part\-time status, exempt or non\-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.
No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non\-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug\-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23\-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901\-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1\-800\-304\-9102 or via email at [email protected]. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
For technical support or questions about Capital One's recruiting process, please send an email to [email protected]
Capital One does not provide, endorse nor guarantee and is not liable for third\-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Capital One, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Capital One AI Hiring
Capital One has 32 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Engineering Manager, AI Software Engineer. Positions span McLean, VA, US, New York, NY, US, San Jose, CA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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