Director – Machine Learning Engineering

Job Description

Job Description – Director, Machine Learning Engineering
Full-time
Leadership Role

1. Job Overview
Details Information
Company Capital One
Job Title Director – Machine Learning Engineering
Team Intelligent Foundations and Experiences (IFX)
Location Bangalore, Karnataka, India
Work Location Code Voyager (94001)
Employment Type Full-time
People Management Experience Minimum 3 years
Programming Experience Minimum 8 years
Machine Learning Experience Minimum 6 years
Distributed Systems Experience Minimum 6 years
Production ML Deployment Experience Minimum 5 years
Education Bachelor’s degree or higher in Computer Science or a related quantitative/engineering field
Salary Not specified
Visa Sponsorship New employment authorization sponsorship not available

2. Team Overview – Intelligent Foundations and Experiences (IFX)
The IFX team is responsible for advancing Capital One’s AI capabilities through Generative AI models, platforms, and intelligent systems.

The team develops and supports:
– Generative AI models and platforms.
– Agentic AI workflows.
– AI guardrails, observability, and evaluation systems.
– Open-weight and proprietary foundation models.
– Shared AI services for enterprise applications.
– Machine learning development and deployment capabilities.
– AI-powered tools to improve software engineering and developer productivity.
3. Job Summary
Capital One is seeking an experienced Director of Machine Learning Engineering to lead large-scale AI and machine learning initiatives.

The role combines advanced machine learning engineering, distributed computing, cloud architecture, production ML deployment, and engineering team leadership.

The selected candidate will drive the design, development, optimization, and deployment of production-ready ML models while managing and mentoring engineering talent.

The Director will collaborate with Product, Architecture, Engineering, and Data Science teams to deliver scalable AI solutions for financial services applications.
4. Key Responsibilities
A. Machine Learning Engineering
– Design, develop, and deliver machine learning models to solve complex business problems.
– Build and evolve intelligent systems using advanced algorithms and ML frameworks.
– Lead large-scale machine learning initiatives.
– Develop optimized models for production environments.
– Apply industry-standard ML frameworks such as PyTorch and TensorFlow.
– Improve model performance, accuracy, scalability, and reliability.
B. Distributed Systems and Data Engineering
– Design and optimize data pipelines for ML model training and evaluation.
– Work with large-scale distributed systems such as Apache Spark and Ray.
– Improve data preparation and processing workflows.
– Build scalable infrastructure for machine learning workloads.
– Optimize data pipelines to support production AI applications.
C. Cloud and Production Deployment
– Design and implement cloud-based ML architectures.
– Deploy and operate production machine learning solutions.
– Manage containerized ML applications using Kubernetes.
– Work with AWS, Google Cloud Platform (GCP), or Microsoft Azure.
– Ensure production services are scalable, reliable, and efficient.
– Optimize cloud infrastructure and ML deployment processes.
D. Engineering Leadership
– Lead and manage machine learning engineering teams.
– Recruit, develop, motivate, and retain high-performing engineering talent.
– Mentor senior engineers, junior engineers, and technical team members.
– Promote engineering best practices across development and modeling lifecycles.
– Encourage innovation, experimentation, and continuous learning.
– Balance hands-on technical contributions with people leadership.
E. Technical Innovation and Thought Leadership
– Stay updated with emerging AI and machine learning technologies.
– Participate in internal and external technology communities.
– Share technical knowledge through conferences, publications, blogs, and open-source contributions.
– Encourage experimentation with new frameworks, algorithms, and engineering approaches.
– Promote best practices in ML engineering and model development.
5. Required Technical Skills
Skill Category Required Technologies / Expertise
Programming Languages Python, Java, Golang (Go), C++
ML Frameworks PyTorch, TensorFlow
Python Libraries Pandas, NumPy, Scikit-learn
Distributed Computing Apache Spark, Ray
Cloud Platforms AWS, GCP, Microsoft Azure
Containerization Docker / containerized ML systems
Container Orchestration Kubernetes
Machine Learning Model development, training, evaluation, optimization
Data Engineering Large-scale data pipelines, data preparation, model training data
Production ML Model deployment, inference, monitoring, production operations
System Architecture Scalable and resilient ML systems

6. Basic Qualifications – Mandatory
Requirement Minimum Experience
People Leadership 3+ years
Programming – Python / Java / Go / C++ 8+ years
Machine Learning using PyTorch or TensorFlow and ML libraries 6+ years
Distributed Systems – Spark / Ray 6+ years
Production ML Deployment and Cloud Operations 5+ years
Kubernetes and Containerized ML Systems Included in the production deployment requirement

Education:

Bachelor’s degree or higher in:
– Computer Science.
– Machine Learning.
– Statistics.
– Economics.
– Operations Research.
– Analytics.
– Mathematics.
– Engineering or another related quantitative field.
7. Preferred Qualifications
Education
– Master’s degree or Doctoral degree in:
– Computer Science.
– Electrical Engineering.
– Mathematics.
– Related technical fields.
Experience
Preferred Skill / Experience Required Experience
Engineering Team Management 5+ years
Large-scale ML System Architecture and Design 3+ years
ML Algorithm and Infrastructure Optimization 5+ years
Advanced Machine Learning Techniques 5+ years
Production-ready ML Data Pipeline Development 7+ years

Advanced Machine Learning Knowledge
Candidates should have experience with:
– Learning methods: Supervised, semi-supervised, unsupervised, and reinforcement learning.
– ML models: Regression, classification, and clustering.
– Model architectures: RNNs, CNNs, LSTMs, and Transformers.
– Model training: Loss functions, hyperparameters, and regularization.
– Model evaluation: Accuracy measurement, underfitting, overfitting, and performance optimization.
Additional Preferred Skills
– Strong technical communication and presentation skills.
– Experience hiring and developing high-performing ML engineers.
– Conference presentations and technical publications.
– Technical blogging and content creation.
– Open-source contributions.
– Patents and research contributions.
– Strong mentoring and leadership capabilities.
8. Leadership and Management Competencies
– Engineering team leadership.
– Talent acquisition and retention.
– Performance coaching and mentoring.
– Technical strategy and architecture.
– Distributed system design.
– Cross-functional collaboration.
– Cloud solution engineering.
– Technical thought leadership.
– Innovation and continuous improvement.
– Communication of complex technical concepts to different audiences.
9. Application and Recruitment Information
Company: Capital One

Location: Bangalore, Karnataka, India

Recruitment Support: Careers@capitalone.com

Accommodation Requests:
– Phone: 1-800-304-9102
– Email: RecruitingAccommodation@capitalone.com
Important: Capital One will not sponsor new employment authorization for this position.

Recruitment Policy: No recruitment agencies.
10. Key Hiring Criteria
– Overall technical profile: Senior ML engineering leader.
– Programming: 8+ years in Python, Java, Go, or C++.
– Machine Learning: 6+ years with PyTorch or TensorFlow.
– Distributed Computing: 6+ years with Spark or Ray.
– Cloud and Production ML: 5+ years deploying and operating ML solutions.
– Leadership: Minimum 3 years of people management.
– Preferred seniority: 5+ years engineering management.
– Advanced expertise: Large-scale ML architecture, model optimization, Kubernetes, cloud infrastructure, and data pipelines.
– Education: Bachelor’s degree mandatory; Master’s or PhD preferred.
Recruitment note: This is a senior Director-level ML Engineering leadership position. Prioritize candidates who combine substantial hands-on machine learning engineering experience with people management, distributed systems, cloud deployment, and production ML architecture. Candidates with only data science, model experimentation, or academic research experience may not meet the production engineering and leadership requirements.