Machine Learning Engineer

About The Role

As a Lead Machine Learning Engineer specializing in Generative AI, Python, Go, and AWS, you will play a pivotal role within the GenAI Workflows Serving team at Capital One. This team is dedicated to designing, building, and deploying large-scale Generative AI applications and Agentic Workflow systems that drive innovation and operational efficiency. You will be responsible for developing cloud-native machine learning solutions that are robust, scalable, and secure, ensuring high availability and low latency for mission-critical AI services. Your expertise will contribute to the continuous improvement of AI infrastructure, enabling the company to stay at the forefront of AI technology. You will collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to deliver impactful AI solutions that solve complex business problems.

Qualifications

The ideal candidate will possess a strong educational background and extensive professional experience in software engineering and machine learning. A Bachelor's degree in computer science, electrical engineering, mathematics, or a related field is required, with a preference for candidates holding a Master's or Doctoral degree. Candidates should have at least six years of experience designing and building data-intensive solutions using distributed computing frameworks, and a minimum of four years programming experience in Python, Scala, Go, or Java. Additionally, at least two years of experience in developing, scaling, and optimizing machine learning systems is essential. Experience with cloud platforms such as AWS, Azure, or Google Cloud, and familiarity with industry-standard ML frameworks like TensorFlow, PyTorch, or scikit-learn, are highly desirable. Leadership experience, particularly managing teams developing ML solutions, is a plus.

Responsibilities

• Design, develop, and deploy Generative AI models and components that address complex business challenges, collaborating closely with product and data science teams.
• Create and implement cloud-native machine learning serving platforms utilizing technologies such as Docker, Kubernetes, KNative, and KServe to ensure scalable and efficient deployment of models.
• Address scaling and high-availability challenges by writing performant application code in Python and Go, automating testing and deployment processes, and validating ML models.
• Implement advanced MLOps and GitOps practices, managing CI/CD pipelines with tools like ArgoCD to streamline model lifecycle management.
• Manage traffic, security, and resilience of high-volume endpoints using service mesh architectures like Istio.
• Monitor, retrain, and maintain models in production environments to ensure optimal performance and compliance with governance standards.
• Construct and optimize data pipelines to feed machine learning models, ensuring data quality and relevance.
• Ensure all code adheres to security standards, reduces vulnerabilities, and aligns with responsible AI practices, including explainability and governance.
• Leverage programming languages such as Python, Go, Scala, or Java to develop resilient and maintainable software solutions.