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

• 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

• 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

Capital One is a nationally recognized and high-tech business banking company, offering better customized consumer and commercial lending and deposit financial services.