Director - Machine Learning Engineering

Voyager (94001), India, Bangalore, Karnātaka Director - Machine Learning Engineering Team Description : The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. Our Generative AI models and platforms empower teams across Capital One to enhance their products by composing powerful, safe and explainable AI capabilities such as agentic workflows, and shared services such as guardrails, observability and evals coupled with foundational model power of open-weight and proprietary frontier models. The team is also delivering capabilities on top of the platform to optimize the software development life-cycle to dramatically enhance developer productivity and model-development life cycle to improve time-to-market of machine learning models. Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking a Director , Machine Learning Engineering who is passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You’ll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala or Java Evangelize best practices in all aspects of the engineering and modeling lifecycles Recruit, nurture and retain top engineering talent Serve as a force-multiplier for the team, balancing deep, hands-on technical contribution and innovation with mentoring and elevating the skills of peers and junior engineers Recruit, nurture, and retain top engineering talent Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities and mentoring other members of the engineering community Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field such as Statistics, Economics, Operations Research, Analytics, Mathematics or Engineering At least 3 years of people leadership experience At least 8 years of experience programming with Python, Java, Golang or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries like Pandas, NumPy and Scikit-learn At least 6 years of experience using and operating large scale distributed systems such as Spark and Ray to prepare AI or Machine Learning data At least 5 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud such as AWS, GCP, or Azure and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics or related field 5+ years of experience managing and leading an engineering team 3+ years of experience architecting and designing resilient, large-scale, production machine learning systems from data preparation to model training and inference 5+ years of experience optimizing ML algorithms, configurations and infrastructure 5+ years of experience working with Machine Learning techniques including Supervised, semi-supervised, unsupervised, and reinforcement learning, model types including Regression, Classification, and Clustering, model Architectures including RNNs, CNNs, LSTMs and Transformers, training concepts such as loss function, hyperparameters, and regularization, and how to evaluate model accuracy and diagnose and address common issues such as underfitting and overfitting 7+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models Ability to communicate complex technical concepts clearly to a variety of audiences Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience hiring and developing high-performing ML engineers with an inspiring leadership style Highly developed interpersonal, presentation, and communications skills Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents At this time, Capital One will not sponsor a new applicant for employment authorization for this position No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).