Senior Director, AI Engineering - Agentic AI Platform
New
C
Capital OneAI banking
Source API remote eligibility restrictions: United StatesFull-TimeDirector
SalaryRemote (Regardless of Location): $286,200 - $326,700 for Sr. Director, AI Engineer. Cambridge, MA: $314,800 - $359,300; McLean, VA: $314,800 - $359,300; New York, NY: $343,400 - $392,000; San Francisco, CA: $343,400 - $392,000; San Jose, CA: $343,400 - $392,000. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).
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Job Details
- Experience
- At least 10 years of experience developing AI and ML algorithms or technologies with a Bachelor's degree, or at least 8 years with a Master's degree; at least 5 years of people leadership experience. Preferred: 7+ years managing and leading an engineering team; 8+ years deploying scalable and responsible AI solutions on cloud platforms.
- Required Skills
- AWSPythonMachine LearningC++
Requirements
- Hold a Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field, with at least 10 years developing AI and ML algorithms or technologies; alternatively, hold a relevant Master's degree with at least 8 years of that experience.
- Have at least 5 years of people leadership experience.
- Preferred: 7+ years managing and leading an engineering team.
- Preferred: 8+ years deploying scalable and responsible AI solutions on AWS, Google Cloud, Azure, or an equivalent private cloud.
- Have experience developing AI/ML algorithms or technologies such as LLM inference, similarity search, VectorDBs, guardrails, or memory using Python, C++, C#, Java, CUDA, or Golang.
- Have experience building agentic AI systems and agentic workflows.
- Have experience leading leaders and multiple AI engineering organizations across product, platform, and applied research.
- Have a record of building enterprise-scale AI platforms for diverse business lines and customer use cases.
- Be able to influence executive leadership on AI investment strategy, organizational design, and risk management priorities.
- Have experience right-sizing models, instance counts, and hardware types for requirements such as context length and token inputs and outputs.
Responsibilities
- Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
- Oversee design, development, testing, deployment, and support of AI software components, including foundation model training, LLM 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, VectorDBs, and PyTorch.
- Develop foundation model optimization techniques to improve scalability, cost, latency, and throughput in production AI systems.
- Contribute to the technical vision and long-term roadmap for foundational AI systems.
- Attract, retain, mentor, and develop AI engineering talent.
- Define and drive enterprise-wide AI strategy across product, research, risk, and data organizations.
- Establish a multi-year roadmap for scalable, reliable, and compliant enterprise AI systems.
- Embed responsible, ethical, transparent, and explainable model practices across lines of business.
- Oversee AI operations, including model lifecycle management, observability, and cost efficiency across production use cases.
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