# Capital One — Senior Manager, AI Engineer (Gen AI Platform Services: Agentic AI, Guardrails, Evaluation)

Generated: September 11, 2026, 9:19 AM EDT  
Canonical provider: Capital One Careers  
Job ID: R999829 / 100107626000  
Original posting: https://www.capitalonecareers.com/job/new-york/senior-manager-ai-engineer-gen-ai-platform-services-agentic-ai-guardrails-evaluation/1732/100107626000  
Posted: September 3, 2026 (posting time unavailable; approximately 8 days before capture)

## Normalized metadata

- Company: Capital One
- Title: Senior Manager, AI Engineer (Gen AI Platform Services: Agentic AI, Guardrails, Evaluation)
- Category / level: Engineering / Senior Manager
- Work model: Not Remote; Capital One structured metadata identifies Remote-Not-Remote and LocationType-People-Center
- Primary locations: New York, NY; San Francisco, CA; San Jose, CA; Cambridge, MA; McLean, VA
- Eligible Keith location: Cambridge, Massachusetts (Greater Boston)
- Compensation: Cambridge and McLean $229,900–$262,400; New York, San Francisco, and San Jose $250,800–$286,200; performance-based incentive compensation and/or long-term incentives may apply
- Travel: Not disclosed
- Applicants: Not disclosed
- Sponsorship: Capital One will consider sponsoring a new qualified applicant

## Full normalized job description

### Overview

At Capital One, the organization is creating responsible and reliable AI systems and applying machine learning to real-time, personalized customer experiences. Investments in technology infrastructure, talent, and machine learning support the development of product experiences and scalable, high-performance AI infrastructure. Applications range from unusual-charge notifications to real-time customer questions, with the goal of bringing emerging AI capabilities to products and services.

The Intelligent Foundations and Experiences (IFX) team works across the company to advance AI science and engineering. It builds and deploys proprietary solutions central to the business, and its models and platforms enable product teams to use AI responsibly and at scale for high-leverage impact.

### Responsibilities

- Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products for associates and customers.
- Oversee design, development, testing, deployment, and support of AI software components including foundation-model training, large-language-model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
- Make build-versus-buy decisions across open-source and SaaS AI technologies including AWS Ultraclusters, Hugging Face, vector databases, NeMo Guardrails, PyTorch, and related tools.
- Introduce state-of-the-art LLM optimization techniques to improve scalability, cost, latency, and throughput of production AI systems.
- Contribute to the technical vision and long-term roadmap for foundational AI systems.
- Attract and retain AI engineering talent, nurture development, and foster continuous learning.

### Ideal candidate

- Builds high-quality systems and acts responsibly.
- Stays current with research, understands scientific publications, and judiciously applies novel techniques in production.
- Develops others through mentoring and coaching; remains hands-on when necessary.
- Brings clarity to ambiguous problems, investigates root causes, and communicates findings clearly.
- Has deep technical foundations across engineering, mathematics, hardware, software, and AI optimization.
- Is resilient and can create new paths toward business goals.

### Basic qualifications

- Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field plus at least six years developing AI/ML algorithms or technologies; or a Master's degree in one of those fields plus at least four years of such experience.
- At least one year of people-leadership experience.

### Preferred qualifications

- Three years managing and leading an engineering team.
- Six years deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud.
- Experience developing AI/ML algorithms or technologies such as LLM inference, similarity search and vector databases, guardrails, and memory using Python, C++, C#, Java, or Go.
- Sustained engagement with AI research and applying new techniques in production.
- Excellent communication and presentation skills and ability to explain complex AI concepts.

### Compensation and other posting details

- Cambridge, MA: $229,900–$262,400 for Sr. Manager, AI Engineering.
- McLean, VA: $229,900–$262,400.
- New York, NY: $250,800–$286,200.
- San Francisco, CA: $250,800–$286,200.
- San Jose, CA: $250,800–$286,200.
- The role may earn performance-based cash and/or long-term incentive compensation.
- Capital One will consider employment-authorization sponsorship for a qualified applicant.
- The role is expected to accept applications for at least five business days.
- Capital One states that it is an equal-opportunity employer and provides benefits subject to eligibility and employment status.

## Fit gate

Positioning track: Technical manager (people leader with hands-on platform accountability).

| Requirement | Weight | Evidence | Score |
|---|---:|---|---:|
| End-to-end AI software design, development, testing, deployment, and support | 3 | Owned AssistX strategy, architecture, core coding, testing, AWS deployment, operations, and production support | 3/3 |
| LLM inference, vector search, guardrails, evaluation, governance, and observability | 3 | Production LLM/RAG/PGVector platform; LLM-as-judge evals, automated guardrails, observability; Responsible AI governance at MassMutual | 3/3 |
| Improve production LLM scalability, cost, latency, and throughput | 3 | Model abstraction and task-based right-sizing for token-cost control; 50× workflow acceleration; production availability evidence | 2/3 |
| Build-versus-buy judgment across open-source and SaaS AI tools | 2 | Evaluated and integrated OpenAI, Bedrock, Claude, Cerebras, open-weight models, LangChain/LangGraph, and PGVector | 2/3 |
| Technical vision and roadmap for foundational AI systems | 3 | Defined reusable AssistX platform architecture and roadmap; led 0-to-1 delivery and domain expansion | 3/3 |
| Recruit, lead, mentor, and retain engineers | 3 | Recruited, trained, and led 11 AI engineers; previously managed engineers and managers across multiple organizations | 3/3 |
| Six years deploying scalable, responsible AI on cloud platforms | 2 | Fifteen years of AWS experience; four years at AWS; cloud AI delivery, MLOps guidance, Responsible AI controls | 3/3 |
| Python/AI engineering, research fluency, and communication | 2 | Hands-on Python/ML/GenAI; Ph.D. research training; 36 invited talks; trained 180+ AWS specialists | 3/3 |

Weighted score: 58 / 63 = 92%.  
Outcome: PASS.  
No hard gap identified.

### Strongest direct matches

- Recent hands-on production platform work covers the central stack: agents, RAG, vector search, guardrails, evaluation, governance, observability, model integration, AWS deployment, and operations.
- Demonstrated player-coach scope: personally built the core platform and recruited/led 11 engineers.
- Deep AWS and Responsible AI experience, including enterprise customer architecture, SageMaker, model transparency, bias, drift, monitoring, and auditability.

### Material caveats

- No source-supported hands-on claim for PyTorch, Hugging Face, NeMo Guardrails, or AWS Ultraclusters.
- Keith's production evidence demonstrates workload acceleration and cost controls, but not the JD's specific large-scale LLM training or hardware-utilization environment.
- The role is not remote. Cambridge, Massachusetts is an approved role location and structured metadata describes a People Center work model; the posting does not state a required weekly on-site cadence.

## Artifact metadata

- Resume: Keith_Steward_Senior_Manager_AI_Engineer_R999829_resume.pdf; 2 Letter-size pages; 36,922 bytes before publication; text, layout, bounds, hyperlinks, and visual rendering validated.
- Cover letter: Keith_Steward_Senior_Manager_AI_Engineer_R999829_cover_letter.pdf; 1 Letter-size page; 22,259 bytes before publication; text, layout, bounds, hyperlinks, and visual rendering validated.
- Google Drive: not used.