Distinguished Engineer - Machine Learning Engineering Job in Capital One

Distinguished Engineer Machine Learning Engineering

Location: Bangalore

Company: Capital One India

About Us

At Capital One India, we re redefining how technology powers financial services. Our teams work in a fast-paced, intellectually rigorous environment to tackle complex business challenges at scale. By harnessing the power of advanced analytics, data science, and machine learning, we create innovative, patentable solutions that transform customer experiences and drive the business forward.

Team Overview: Machine Learning Experience (MLX)

The MLX team leads Capital One s mission to build scalable, well-managed ML systems and platforms. We empower teams across the enterprise to develop, govern, and deploy machine learning models efficiently, securely, and at scale. From automated model governance to observability platforms, MLX enables end-to-end ML lifecycle management laying the foundation for AI-driven innovation across the organization.

Role Overview

We re looking for a Distinguished Engineer Machine Learning Engineering to join our MLX team. In this high-impact role, you'll architect and implement the platforms and tools that support model observability, automated governance, and ML model deployment at scale. This is an opportunity to drive enterprise-wide innovation and shape how ML is integrated into Capital One s core business systems.

What You ll Do

• Design and build systems that capture and analyze large-scale model and feature metadata, including training metrics and runtime performance, to power model observability and governance automation.
• Partner with cross-functional teams including product managers, designers, and platform engineers to create scalable solutions that accelerate ML model lifecycle management.
• Lead efforts to enable automated governance decisions for ML models, ensuring compliance, auditability, and operational integrity.
• Architect and implement high-performance data pipelines that feed ML models with real-time and batch data.
• Contribute to the design and implementation of cloud-native ML systems using tools such as AWS, Kubernetes, and Terraform.
• Write clean, scalable, production-grade code in languages like Python, Go, or Java.
• Implement CI/CD pipelines, testing frameworks, and monitoring systems for ML applications.
• Drive the adoption of best practices in ML Ops, observability, and platform resilience.

Basic Qualifications

• Master s Degree in Computer Science or related field.
• 15+ years of experience in software engineering or solution architecture.
• 10+ years building data-intensive, distributed computing systems.
• 10+ years programming in Python, Go, or Java.
• 8+ years of hands-on experience with industry-leading ML frameworks (e.g., Scikit-learn, TensorFlow, PyTorch, Dask, Spark).

Preferred Qualifications

• PhD or Master's in Computer Science, Electrical Engineering, Mathematics, or related field.
• 5+ years of experience building, scaling, and optimizing production ML systems.
• Deep expertise in data preparation, feature engineering, and ML pipeline optimization.
• 10+ years writing performant, maintainable, and resilient production code.
• Strong experience deploying ML solutions on public cloud platforms (AWS, Azure, GCP).
• Expertise in distributed systems, file systems, or multi-node databases.
• Open-source contributor to ML tools or libraries.
• Published work in ML (papers, patents, blogs, etc.).
• 5+ years of experience in ML Ops (using MLflow, TFX, Kubeflow, etc.).
• Experience with LLMs and Generative AI applications (open-source or commercial models).
• Proven experience designing production-ready observability platforms for ML applications.

Why Join Us

• Be at the forefront of building scalable, secure, and enterprise-grade ML platforms.
• Shape the future of AI and ML adoption in a top-tier financial institution.
• Collaborate with world-class engineers and data scientists.
• Solve real-world problems with high business impact.
• Thrive in a diverse, inclusive, and innovation-focused culture.