AI Engineer 5 (AI Foundations, LLM Core and Agentic AI)
for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections if posting has real specific content. We must not repeat job title as heading. No Job ID etc. Remove boilerplate.
We need to parse content: The posting includes:
- Intro paragraph about Capital One AI.
- Team Description: The Intelligent Foundations and Experiences (IFX) team...
- What You’ll Do: list of duties (partner with cross-functional team, design/develop/test/deploy/support AI software components, leverage stack, invent techniques, contribute to technical vision, design/implement/optimize multi-model orchestration pipelines, establish and lead cost-performance governance reviews, lead team design councils, mentor Principal and Manager-level AI engineers)
- The Ideal Candidate: bullet-like paragraphs describing traits (love to build systems, passion for staying abreast, adapt quickly, courage to share new ideas, deeply technical, resilient trailblazer)
- Basic Qualifications: Bachelor's degree plus 6 years experience, or Master's plus 4 years, at least 6 years programming with Python, Go, Scala, CUDA, or Java
- Preferred Qualifications: list of many items (experience leading development AI systems with tradeoff decisions, 7 years deploying scalable AI on cloud, experience designing/developing/delivering/supporting complex AI systems, experience developing AI/ML algorithms using languages, experience optimizing training/inference software, experience building agentic AI systems, passion for staying abreast, excellent communication, experience architecting integrating heterogeneous AI systems, experience defining/enforcing standards for ethical AI deployment, demonstrated ability to balance model performance and operational cost, experience right-sizing models, instance counts, hardware types)
- Salary info: location-based ranges.
- Benefits: mention comprehensive competitive inclusive set of health, financial and other benefits, link to website.
- Also mention performance based incentive compensation.
- Also mention sponsorship.
- Also mention EEO etc (remove).
- Also mention application instructions (remove).
- Also mention accommodation contact (maybe keep? It's boilerplate? It's about accommodation request; might be considered legal/EEO? Probably remove as it's accommodation info but could be considered part of benefits? The instruction says remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate. Accommodation request is likely legal/EEO, so remove.
- Also mention technical support email (maybe remove as it's recruiting process? Might be considered boilerplate; remove.)
- Also mention disclaimer about third-party products (remove).
- Also mention note about Canada/UK/Philippines (remove).
We need to preserve all substantive information: intro, team description, responsibilities, ideal candidate traits, basic qualifications, preferred qualifications, pay, benefits, incentives, sponsorship maybe? Sponsorship is substantive: "Capital One will consider sponsoring a new qualified applicant for employment authorization for this position." Keep that.
We need to decide sections: About the role (maybe combine intro + team description). Responsibilities (What You’ll Do). Requirements (maybe Basic Qualifications). Qualifications (Preferred Qualifications). Skills (maybe list of technical skills from qualifications? Could be separate but we can put under Skills if we have content). Benefits (benefits + incentives). Pay (salary ranges). Schedule (not provided, so omit). Also maybe include a section for "Sponsorship" but not in allowed headings; we could fold into About the role or Benefits? Probably include in About the role as a short paragraph.
We must not create sections with filler like "not specified". So only include sections with real content.
Thus sections:
About the role
: include intro paragraph(s) about Capital One AI, team description, sponsorship note.
Responsibilities
: list of duties from What You’ll Do.
Requirements
: Basic Qualifications.
Qualifications
: Preferred Qualifications.
Skills
: maybe we can extract technical skills list from qualifications? But we already have them in Requirements and Qualifications; maybe we can skip Skills to avoid thin section. However we could create Skills summarizing programming languages, tools, etc. But we must not invent or summarize away detail; we could list them as they appear in qualifications. But they'd be duplicate. Better to omit Skills if not enough distinct content. The instruction: Prefer a few substantive sections over many thin ones. So we can omit Skills.
Benefits
: benefits description + performance based incentive compensation.
Pay
: salary ranges per location.
We must not include Schedule.
Now format: Use - for any list of duties/requirements/benefits. For prose use
.
We need to preserve all substantive information; do not invent or summarize away detail. So we need to keep the exact wording? We can keep close but not necessarily verbatim? Probably we should keep the substance but can rephrase slightly? Safer to keep original sentences.
We'll produce:
About the role
At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent — along with our deep experience in machine learning — position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.
Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact.
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
Responsibilities
- Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
- Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
- Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more.
- Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
- Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
- Design, implement and optimize multi-model orchestration pipelines - integrating LLMs, vector search, and domain-specific models into unified systems.
- Establish and lead cost-performance governance reviews across AI systems, tracking GPU utilization, model throughput, and inference cost efficiency.
- Lead team design councils or design review boards to ensure technical consistency and compliance with AI engineering standards.
- Mentor Principal and Manager-level AI engineers, fostering cross-domain learning and elevating organizational technical maturity.
Requirements
- Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies.
- At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java.
Qualifications
- Experience leading development AI systems with tradeoff decisions around cost, latency, throughput and accuracy.
- 7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud).
- Experience designing, developing, delivering, and supporting complex AI systems.
- Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang.
- Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost.
- Experience in building agentic AI systems and agentic workflows.
- Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production.
- Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers.
- Experience architecting and integrating heterogeneous AI systems - including rule-based, retrieval-augmented, and generative components - into unified production pipelines.
- Experience defining and enforcing standards for ethical AI deployment, including explainability, fairness, and human-in-the-loop review processes.
- Demonstrated ability to balance model performance and operational cost through dynamic inference strategies and model compression.
- Experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs).
Benefits
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 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.
Pay
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.
- Cambridge, MA: $229,900 - $262,400 for AI Engineer 5
- McLean, VA: $229,900 - $262,400 for AI Engineer 5
- New York, NY: $250,800 - $286,200 for AI Engineer 5
- San Jose, CA: $250,800 - $286,200 for AI Engineer 5
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.
We must ensure no extra sections like Schedule
. We need to preserve all substantive information; do not invent or summarize away detail. So we need to keep the exact wording? We can keep close but not necessarily verbatim? Probably we should keep the substance but can rephrase slightly? Safer to keep original sentences. We'll produce:
About the role
At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent — along with our deep experience in machine learning — position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.
Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact.
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
Responsibilities
- Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
- Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
- Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more.
- Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
- Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
- Design, implement and optimize multi-model orchestration pipelines - integrating LLMs, vector search, and domain-specific models into unified systems.
- Establish and lead cost-performance governance reviews across AI systems, tracking GPU utilization, model throughput, and inference cost efficiency.
- Lead team design councils or design review boards to ensure technical consistency and compliance with AI engineering standards.
- Mentor Principal and Manager-level AI engineers, fostering cross-domain learning and elevating organizational technical maturity.
Requirements
- Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies.
- At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java.
Qualifications
- Experience leading development AI systems with tradeoff decisions around cost, latency, throughput and accuracy.
- 7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud).
- Experience designing, developing, delivering, and supporting complex AI systems.
- Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang.
- Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost.
- Experience in building agentic AI systems and agentic workflows.
- Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production.
- Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers.
- Experience architecting and integrating heterogeneous AI systems - including rule-based, retrieval-augmented, and generative components - into unified production pipelines.
- Experience defining and enforcing standards for ethical AI deployment, including explainability, fairness, and human-in-the-loop review processes.
- Demonstrated ability to balance model performance and operational cost through dynamic inference strategies and model compression.
- Experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs).
Benefits
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 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.
Pay
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.
- Cambridge, MA: $229,900 - $262,400 for AI Engineer 5
- McLean, VA: $229,900 - $262,400 for AI Engineer 5
- New York, NY: $250,800 - $286,200 for AI Engineer 5
- San Jose, CA: $250,800 - $286,200 for AI Engineer 5
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.
We must ensure no extra sections like Schedule