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Lead Machine Learning Engineer (Manager IC) at Capital One. In Risk Tech, you will work with our GRC team and partners across the company to build and deploy proprietary solutions for Risk management that are powered by state-of-the-art AI technology.
Job Responsibility
Partner with a cross-functional team of engineers, data scientists, product managers, and designers to deliver AI-powered products that change how our associates work and provide value to our customers
Design, develop, test, deploy, and support AI software components utilizing machine learning models, including model evaluation and experimentation, large language model inference, similarity search, guardrails, governance, observability and agentic AI
Fine-tune, develop and evaluate machine learning and foundation models
Collaborate as part of a cross-functional Agile team to create and enhance software that utilizes state-of-the-art AI and ML capabilities
Contribute thought leadership and technical vision to the long term roadmap of pioneering AI systems at Capital One
Leverage a broad stack of Open Source and SaaS AI technologies
Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues
Retrain, maintain, and monitor models in production
Construct optimized data pipelines to feed ML models
Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI
Requirements
Bachelor's Degree
At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
At least 4 years of experience programming with Python, Scala, or Java
At least 2 years of experience building, scaling, and optimizing ML systems
Nice to have
Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
7+ years of experience designing, developing, delivering, and supporting AI services at scale
3+ years of experience building production-ready data pipelines that feed ML models
3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
3+ years of experience developing AI and ML algorithms or technologies using Python
2+ years of experience with Retrieval Augmented Generation (RAG)
2+ years of experience with data gathering and preparation for ML models
2+ years of people leader experience
1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion
Experience deploying scalable AI/ML solutions in a public cloud such as AWS Bedrock, Google Cloud, Azure
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
What we offer
performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
comprehensive, competitive, and inclusive set of health, financial and other benefits