AI Platform Director - Scalable, Responsible AI

Capital One Group

San Jose (CA)

Remote

USD 343,000 - 392,000

Full time

3 days ago

Be an early applicant

Get a reply from this employer

Your resume and cover letter, tailored to exactly what they’re hiring for.

Get past ATS filters

Benefits offered by this job

Remote eligibility

Job summary

Capital One is seeking a Senior Director of AI Engineering for the Agentic AI Platform with remote eligibility. Lead AI software components, model optimization, and enterprise-wide AI strategy while mentoring a high-performing team.

Emphasizes responsible AI and scalable infrastructure across Capital One. The role demands 10+ years in AI/ML development and 5+ years of people leadership, with cloud and platform experience, and strong cross-functional collaboration.

Qualifications

Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 10 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 8 years of experience developing AI and ML algorithms or technologies
At least 5 years of people leadership experience
7+ years of experience managing and leading an engineering team
8+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
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 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
Demonstrated experience leading leaders and multiple AI engineering organizations across product, platform and applied research domains
Proven success in building enterprise-scale AI platforms and capabilities serving diverse business lines and customer use cases
Ability to influence executive leadership on AI investment strategy, organizational design and risk management priorities
Experience establishing external partnerships or thought leadership presence in AI industry, academia, or open-source communities
Experience right-sizing models, instance counts, and hardware types given requirements

Responsibilities

Deliver AI-powered products with cross-functional teams.
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, etc.
Make high judgment build-vs-buy decisions across 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.
Attract and retain top talent in the AI industry and nurture personal and professional development for your team. Foster a culture of learning and staying abreast of the state-of-the-art in AI.
Define and drive enterprise-wide AI strategy, aligning product, research, risk, and data organizations around shared technical and ethical principles
Establish a multi-year roadmap for scalable, reliable, and compliant AI systems that serve as shared infrastructure across the enterprise
Champion Responsible-AI, ensuring ethical, transparent and explainable model practices are embedded in all lines of business
Oversee the scaling of AI operations - including model lifecycle management, observability, and cost efficiency - across hundreds of production use cases