Full Stack Engineer 4 - MLOps
$197k - $225k • McLean, VA • Full-time
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Capital One is seeking Software Engineers specializing in full-stack architecture and MLOps to bridge the gap between software engineering, cloud infrastructure, and machine learning. As a Software Engineer, you will aid engineering teams in building scalable ML infrastructure, productionizing data pipelines, and driving technology transformation. The Bank Tech Data, Decisioning, & Market Services Domain builds the infrastructure that turns data into smart, fast decisions, integrating data and AI capabilities into a cohesive platform to support business and capital market teams. The focus is on creating a modern data architecture to leverage information responsibly and provide predictive insights for better client outcomes, with imperatives centered on evolving predictive insights and scaling AI-driven decisioning models.
Responsibilities
- Own the end-to-end MLOps lifecycle by taking research models, 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.
- Write performant, maintainable code using Python, 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, TensorFlow, Spark), and interactive AI tools to automate model deployment and manage enterprise-grade ML platforms.
Requirements
- Bachelor's Degree or higher in Computer Science or a related quantitative field.
- At least 4 years of experience in software engineering.
- 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.
- Master’s Degree in Computer Science or a related field (preferred).
- 4+ years of experience building production-ready data pipelines that feed ML models (preferred).
- 3+ years of experience developing performant, resilient, and maintainable code (preferred).
- 7+ years of experience in at least one of the following: Java, SQL, Python, or Go (preferred).
- 2+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow (preferred).
- 4+ years of experience in open source frameworks (preferred).
- 1+ years of people management experience (preferred).
- 2+ years of experience in Agile practices (preferred).
- Experience collaborating with data science teams and technical stakeholders (preferred).
- Experience leveraging interactive AI tooling to accelerate productivity (preferred).
Benefits
- Performance based incentive compensation (cash bonus(es) and/or long term incentives).
- Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.