Sr. Distinguished Engineer

1 month ago

Boston, United States Capital One Full time

Center 1 (19052), United States of America, McLean, Virginia

Sr. Distinguished Engineer - Platform Operations

At Capital One, we believe that AI and machine learning represent the biggest opportunity in financial services today, and is a chance to revolutionize the industry with more real-time personalized experiences than it was ever possible. Our mission is to use the power of machine learning to deliver better financial services to our customers by creating trustworthy, reliable and human-in-the-loop systems.

From informing customers about unusual charges to answering their questions in real time, our AI/ML capabilities are bringing humanity and simplicity to banking. Because of our investments in public cloud infrastructure that provides on-demand compute and storage for machine learning and our principled approach to building enterprise platforms led by our best talent, we are now uniquely positioned to harness the power of generative AI that few other organizations can. Capital One's commitment to AI has sponsorship from the CEO, the Board of Directors, and the executive committee of the company.

We are committed to building world-class applied science and engineering teams, on the foundations of our industry leading data and AI/ML capabilities with breakthrough product experiences. The Vice President, Platform Operations will lead and optimize our Machine Learning & Artificial Intelligence platform operations. This executive will report into the Senior Vice President, Head of Machine Learning Experience in our engineering organization.

At Capital One, diversity, inclusion and belonging are valued at our core. We empower our associates to do great work by creating an inclusive culture - we call this our Culture of Belonging, and it rests at the heart of our Diversity, and Inclusion & Belonging (DIB) efforts. As this role will serve as a member of our leadership team within our Enterprise Data Machine Learning organization, and will be responsible for leading, coaching, and mentoring our engineers, it is of paramount importance this individual values diverse perspectives, fosters collaboration and encourages innovative ideas - and can create a place where associates of all backgrounds can thrive by bringing their most authentic selves to work.

Senior Distinguished Engineers (DEs) are individual contributors who strive to be diverse in thought so we visualize the problem space. At Capital One, we believe diversity of thought strengthens our ability to influence, collaborate and provide the most innovative solutions across organizational boundaries. Distinguished Engineers will significantly impact our trajectory and devise clear roadmaps to deliver next generation technology solutions.


Senior DEs are:
  • Deep technical experts and thought leaders that help accelerate adoption of the very best engineering practices, while maintaining knowledge on industry innovations, trends and practices
  • Visionaries, collaborating on Capital One's toughest issues, to deliver on business needs that directly impact the lives of our customers and associates
  • Role models and mentors, helping to coach and strengthen the technical expertise and know-how of our engineering and product community
  • Evangelists, both internally and externally, helping to elevate the Distinguished Engineering community and establish themselves as a go-to resource on given technologies and technology-enabled capabilities

Responsibilities:

  • Play a pivotal role in setting the roadmap and overseeing the day-to-day management of our AI and ML platforms including: setting strategies and overseeing container management in public cloud (AWS), cloud resource provisioning, ensuring low latency, high availability of cloud resources, cloud optimization, etc.
  • Maintain a deep understanding of the technical aspects of the platform, including infra, algorithms, APIs and integrations. Provide operations leadership to the engineering and production teams.
  • Implement robust processes and operations dashboard to monitor platform performance, user feedback, and adherence to service level agreements (SLAs), observability, resiliency, and key operational metrics in real time.
  • Collaborate with cyber, technology risk management, security and compliance teams to understand the company cyber, risk and compliance requirements. Work closely with product and engineering to ensure the platform adheres to industry best practices, corporate cyber and tech risk management standards.
  • Implement automation and dashboards to visualize vulnerabilities, platform incidents, cloud controls compliance, cloud resource utilization etc. to enable proactive decision making, and risk mitigation.
  • Work closely with executive leadership and buy-ins to develop a long-term vision and roadmap for the platform operations enhancements.
  • Build a high performing operations team, recruiting world class SREs, production engineers, data engineers, groom, and retain talent on team.

Basic Qualifications:

  • Bachelor's Degree
  • At least 9 years of experience managing Platform, infrastructure operations or Site Reliability Engineering.
  • At least 5 years' experience with public cloud technologies

Preferred Qualifications:

  • Master's Degree in "STEM" field (Science, Technology, Engineering, or Mathematics)
  • 5+ years of experience in managing large-scale, high-performance, distributed systems as a Site Reliability Engineer or a product engineer
  • 5+ years experience in setting up and scaling observability platform, providing monitoring and telemetry support and creating Operational health dashboards
  • 3+ years experience in building systems and solutions within a regulated environment
  • 3+ years of experience in Artificial Intelligence, Machine Learning or Cloud infrastructure
  • 3+ years experience with managing distributed systems, multi-tenant, micro services, and container orchestration (Kubernetes)
  • Experience partnering with technology peers responsible for data architecture and distributed computing infrastructure or platforms
  • 5+ years of experience with machine learning lifecycle(building, training models, serving models, setting up cloud infrastructure or data pipelines) and familiarity with major Machine Learning frameworks
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