Lead Machine Learning Engineer, Gen AI, Python, Go, AWS

Capital One

Lead Machine Learning Engineer focused on Generative AI applications and cloud-native platforms at Capital One. Collaborating with AI/ML teams to innovate and implement robust machine learning solutions.

Posted 4/18/2026full-timeNew York City • California, Massachusetts, New York, Virginia • 🇺🇸 United StatesSenior💰 $197,300 - $245,600 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoGoogle Cloud PlatformJavaKubernetesPythonPyTorchScalaScikit-LearnSparkTensorflow

About the role

Key responsibilities & impact

Design, build, and deliver GenAI models and components that solve complex business problems
Design and implement cloud-native ML Serving Platforms leveraging technologies like Docker, Kubernetes, KNative, and KServe
Solve complex scaling and high-availability problems by writing and testing performant application code in Python and Go-lang
Implement advanced MLOps and GitOps practices for continuous integration and continuous deployment (CI/CD) using tools like ArgoCD
Leverage service mesh architectures like Istio to manage traffic, enhance security, and ensure resilience for high-volume serving endpoints
Retrain, maintain, and monitor models in production
Construct optimized, scalable data pipelines to feed ML models
Ensure all code is well-managed to reduce vulnerabilities and follow best practices in Responsible and Explainable AI

Requirements

What you’ll need

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, Go or Java
At least 2 years of experience building, scaling, and optimizing ML systems
Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field (Preferred)
3+ years of experience building production-ready data pipelines that feed ML models (Preferred)
3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow (Preferred)
2+ years of experience developing performant, resilient, and maintainable code (Preferred)
2+ years of experience with data gathering and preparation for ML models (Preferred)
2+ years of people leader experience (Preferred)
1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation (Preferred)
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform (Preferred)
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance (Preferred)

Benefits

Comp & perks

comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being
performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonGoScalaJavaMLOpsGitOpsCI/CDdata pipelinesML frameworkscloud-native ML Serving Platforms

Soft Skills

people leadershipteam leadershipproblem solvingcommunicationcollaboration

Certifications

Bachelor's DegreeMaster's DegreeDoctoral Degree