Director - Machine Learning Engineering
Salary not listed • Bangalore, In • Full-time
Posted 3d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
The Intelligent Foundations and Experiences (IFX) team is central to bringing AI at Capital One to life, empowering teams with generative AI models and platforms. These capabilities include agentic workflows, guardrails, observability, and evals, leveraging both open-weight and proprietary frontier models. The team also focuses on optimizing the software development lifecycle to enhance developer productivity and improve the time-to-market for machine learning models. We are looking for a Director, Machine Learning Engineering passionate about using cutting-edge open-source frameworks, advanced algorithms, and emerging technologies to drive major AI transformations and scale production models across Capital One.
Responsibilities
- Deliver ML models and software components to solve business problems in financial services, collaborating with Product, Architecture, Engineering, and Data Science teams.
- Drive the creation and evolution of ML models and software for state-of-the-art intelligent systems.
- Lead large-scale ML initiatives with a customer-centric approach.
- Leverage cloud-based architectures and technologies for optimized ML model delivery at scale.
- Optimize data pipelines for feeding ML models.
- Evangelize best practices across engineering and modeling lifecycles.
- Recruit, nurture, and retain top engineering talent.
- Act as a force multiplier by balancing hands-on technical contribution with mentoring and skill development.
- Stay on top of tech trends, experiment with new technologies, and participate in technology communities.
Requirements
- Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field.
- At least 3 years of people leadership experience.
- At least 8 years of programming experience with Python, Java, Golang, or C++.
- At least 6 years of Machine Learning experience using PyTorch or Tensorflow and libraries like Pandas, NumPy, and Scikit-learn.
- At least 6 years of experience using and operating large-scale distributed systems like Spark and Ray for AI/ML data preparation.
- At least 5 years of experience deploying and operating ML solutions in production within cloud environments (AWS, GCP, Azure) and using Kubernetes.
- Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field (preferred).
- 5+ years of experience managing and leading an engineering team (preferred).
- 3+ years of experience architecting and designing large-scale, production ML systems from data preparation to inference (preferred).
- 5+ years of experience optimizing ML algorithms, configurations, and infrastructure (preferred).
- 5+ years of experience with ML techniques including Supervised, semi-supervised, unsupervised, and reinforcement learning, model types (Regression, Classification, Clustering), architectures (RNNs, CNNs, LSTMs, Transformers), training concepts, and model evaluation (preferred).
- 7+ years of experience designing, implementing, and scaling production-ready data pipelines for ML models (preferred).
- Ability to communicate complex technical concepts clearly to diverse audiences (preferred).
- Experience driving impacts in the ML industry through presentations, papers, blogs, or open-source contributions (preferred).
- Experience hiring and developing high-performing ML engineers with an inspiring leadership style (preferred).
- Highly developed interpersonal, presentation, and communication skills (preferred).