Lead Machine Learning Engineer (Finance Tech - AI Enablement)

Capital One•McLean, MA

About The Position

At Capital One, we are changing banking for good by creating responsible and reliable AI-powered systems. Our investments in technology infrastructure and world-class talent — along with our deep experience in machine learning — position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine exceptional products for our customers. In this role at Finance Tech, you will be playing a lead role as part of Finance Tech horizontal AI enablement teams in developing/deploying best practices and end user facing AI usecases for Finance LOB. The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:

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 Haves

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

Responsibilities

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.