Full Stack Engineer - MLOps

Full Stack Engineer 4 - MLOpsDo you love building and pioneering in the 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 solve real problems and meet real customer needs. We are seeking Software Engineers who specialize in full-stack architecture and MLOps to bridge the gap between software engineering, cloud infrastructure, and machine learning.

As a Capital One Software Engineer, you’ll aid engineering teams in building scalable ML infrastructure, product ionizing data pipelines, and driving a major technology transformation within Capital One.  The Bank Tech Data, Decisioning, & Market Services Domain serves as the analytical engine of the bank, building the infrastructure that turns data into smart, fast decisions. We integrate data and AI capabilities into a single, cohesive platform that supports our business and capital market teams.

Our focus is on creating a modern data architecture that allows us to leverage information responsibly, providing the predictive insights needed to deliver better outcomes for our clients. Looking ahead, our imperatives are centered on evolving our predictive insights and scaling our AI-driven decisioning models.  
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* What You’ll Do:

*** Own the end-to-end MLOps lifecycle by taking research models built by data scientists, building scalable infrastructure, and deploying them into production microservices.
* Design, build, and optimize automated data pipelines, model serving platforms, and continuous monitoring systems for high-stakes applications like real-time account takeover fraud detection and AI-driven vulnerability management.
* Write performant, maintainable code using Python (with Java/Scala as secondary stack components) anchored in strong software engineering practices, vulnerability remediations, and system resiliency.
* Balance hands-on software development and MLOps engineering with technical delivery leadership, guiding engineers, partnering with product management, and coordinating cross-functional rollouts.
* Harness cloud infrastructure (AWS), container orchestration (Docker/Kubernetes), ML tooling (PyTorch, Tensor Flow, Spark), and interactive AI tools to automate model deployment and manage enterprise-grade ML platforms.  
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* Basic Qualifications:

*** Bachelor's Degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
* At least 4 years of experience in software engineering (Internship experience does not apply)
* At least 2 years of experience with public cloud platforms (AWS, GCP, Azure), cloud-based productivity tools, or virtualization technologies
* At least 2 years of experience building, scaling, and optimizing ML systems  
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* Preferred Qualifications:

*** Master’s Degree in Computer Science or a related field
* 4+ years of experience building production-ready data pipelines that feed ML models
* 3+ years of experience developing performant, resilient, and maintainable code    7+ years of experience in at least one of the following:
Java, SQL, Python, or Go
* 2+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or Tensor Flow
* 3+ years of experience developing performant, resilient, and maintainable code
* 3+ years of experience with data gathering and preparation for ML models
* 4+ years of experience in open source frameworks
* 1+ years of people management experience
* 2+ years of experience in Agile practices
* Experience collaborating with data science teams and technical stakeholder
* Experience…