AI Engineer 5 (MLX, Agentic AI, Gen AI platform Services)

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. No Job ID. Remove boilerplate, EEO, etc. Keep substantive info. We need to parse the posting. Key sections: - Intro paragraph: "At Capital One, we are creating responsible and reliable AI systems..." That's about the role/company. Could go under About the role. - Team Description: description of IFX team. Could be part of About the role. - What You’ll Do: list of duties. That's Responsibilities. - The Ideal Candidate: bullet list of traits. Could be Requirements or Qualifications? Usually "Ideal Candidate" is qualifications/skills. We'll treat as Requirements (maybe Qualifications). But we also have Basic Qualifications and Preferred Qualifications sections. Those are explicit qualifications. - Basic Qualifications: list. - Preferred Qualifications: list. - Pay: salary ranges per location. That's Pay. - Benefits: mention of comprehensive benefits, performance based incentive compensation, health, financial etc. That's Benefits. - Schedule: Not mentioned explicitly. No schedule info. So we should not include Schedule section. - Skills: Could be derived from Preferred Qualifications (experience with certain tech). But we might combine into Qualifications or Skills. The instruction: Use headings for sections like Skills if there is real specific content. We have specific skills: programming languages, AI/ML techniques, etc. Could put under Skills. We need to avoid filler like "commensurate with experience". Not present. We must preserve all substantive info; not invent or summarize away detail. So we need to include all details from Basic Qualifications, Preferred Qualifications, Pay, Benefits, Responsibilities, About the role, Team Description, etc. We must not include boilerplate like EEO, accommodation, etc. We need to output only HTML fragment, no markdown fences. Let's structure:

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.

That seems good.

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

We could combine Ideal Candidate traits and Basic Qualifications? But Basic Qualifications is separate heading. Perhaps we make Requirements = Basic Qualifications, and Qualifications = Preferred Qualifications? Or we could have Requirements (basic) and Qualifications (preferred). The instruction says use headings for sections like Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we can have both Requirements and Qualifications. Thus:

Requirements

Basic Qualifications:

• 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.

But we need to preserve all info: The Basic Qualifications text includes two options. We'll keep as is.

Qualifications

Preferred 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).

We could also include Ideal Candidate traits as Skills? But we already have many skill-like items in Preferred Qualifications. The Ideal Candidate traits are more about personal attributes. Could be placed under Skills or Requirements. Since we already have Requirements (basic) and Qualifications (preferred), we could add a Skills section for those personal attributes. However, we must not create thin sections. The Ideal Candidate traits are substantive; we could incorporate them into Qualifications or Skills. Let's make a Skills section for those personal attributes. But we already used Qualifications for Preferred Qualifications. Could rename that to Preferred Qualifications and then Skills for personal attributes. However the instruction says use headings like Skills if there is real specific content. We have personal attributes: love to build systems, take pride, passion for staying abreast of latest research, ability to intuitively understand scientific publications, adapt quickly, thrive on bringing clarity to big undefined problems, love asking questions, dig deep, articulate findings concisely, courage to share new ideas, deeply technical, strong foundation in engineering and mathematics, expertise in hardware, software, AI, resilient trailblazer, can forge new paths. Those are specific. Thus we can have:

Skills

• Love to build systems, take pride in the quality of work, and share passion to do the right thing.
• Passion for staying abreast of the latest research, and ability to intuitively understand scientific publications and judiciously apply novel techniques in production.
• Adapt quickly and thrive on bringing clarity to big, undefined problems.
• Love asking questions and digging deep to uncover the root of problems and can articulate findings concisely with clarity.
• Have the courage to share new ideas even when they are unproven.
• Deeply technical: strong foundation in engineering and mathematics, expertise in hardware, software, and AI enables seeing and exploiting optimization opportunities others miss.
• Resilient trailblazer who can forge new paths to achieve business goals when the route is unknown.

Now Benefits: We have: "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." We should include that as Benefits. Thus:

Benefits

• Comprehensive, competitive, and inclusive set of health, financial and other benefits supporting total well-being.
• Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
• Performance-based incentive compensation possible, including cash bonus(es) and/or long term incentives (LTI); discretionary or non-discretionary depending on plan.

Pay: We have salary ranges per location. Also mention that this role is eligible for performance based incentive compensation (already in Benefits). Pay section should include the salary ranges. Thus:

Pay

• 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 Francisco, CA: $250,800 - $286,200 for AI Engineer 5
• San Jose, CA: $250,800 - $286,200 for AI Engineer 5
• Note: 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 will be reflected in the offer letter.

We should not include the sentence about "This role is also eligible to earn performance based incentive compensation" in Pay; we already put in Benefits. Schedule: No info, so omit. Now ensure we didn't include any boilerplate like EEO, accommodation, etc. We