ML Engineer, Sr.

About Us
Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions, and government entities across more than 200 countries and territories. Our mission is dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale—tackling meaningful challenges, growing your skills, and seeing your contributions impact lives around the world. Join Visa and do work that matters— to you, to your community, and to the world. Progress starts with you.
About The Role
The Senior Machine Learning (ML) Engineer is responsible for designing, building, and maintaining the ML platform infrastructure that powers AI/ML applications across the organization. This role is ideal for a hands-on engineer with practical experience in cloud technologies such as AWS, SageMaker, Kubernetes, GPU orchestration, Infrastructure as Code, and MLOps practices. The successful candidate will focus on creating scalable, secure, and reliable platforms that enable Data Scientists and AI Engineers to efficiently move models from research to production. Key responsibilities include designing cloud and on-prem infrastructure, modernizing legacy ML pipelines, and developing platform tooling to enhance productivity. The role requires collaboration with cross-functional teams including Data Scientists, AI Engineers, security, and infrastructure teams to ensure seamless integration of AI/ML solutions into production systems. The candidate should possess digital fluency and familiarity with emerging AI technologies such as Generative AI tools, ChatGPT, GitHub Copilot, and other AI-enabled productivity platforms. The position involves contributing to architectural decisions, implementation standards, and best practices to ensure the robustness of the ML platform.
Qualifications
2+ years of relevant work experience with a Bachelor's degree in Computer Science, Engineering, or related field, OR 5+ years of relevant work experience
Experience designing, building, and maintaining scalable ML platform infrastructure for AI/ML applications
Proficiency with AWS services such as EC2, S3, EKS, SageMaker, IAM, VPC, and CloudWatch
Experience managing Kubernetes clusters and containerized ML workloads using Docker
Knowledge of ML pipeline and orchestration tools such as Kubeflow, Airflow, MLflow, or similar platforms
Experience with Infrastructure as Code tools like Terraform, CloudFormation, or similar
Proficiency in developing CI/CD pipelines for ML models and infrastructure deployment
Strong understanding of secure cloud architecture, including IAM roles, VPCs, and networking best practices
Proficiency in Python and shell scripting for automation and tooling
Experience collaborating with Data Scientists and AI Engineers to deploy models into production
Knowledge of Generative AI, LLMs, and GenAI infrastructure
Experience with GPU orchestration for training, inference, and workload optimization
Familiarity with ML serving frameworks such as Triton, KServe, vLLM, or TensorRT-LLM
Experience with hybrid cloud or on-prem/cloud ML infrastructure
Understanding of distributed computing frameworks like Spark
Experience with AI-assisted tools such as GitHub Copilot, ChatGPT, or similar platforms
Proven ability to lead and mentor junior engineers and implement key platform modules
Responsibilities
Lead and deliver specific platform engineering initiatives as a Senior ML Engineer
Provide technical guidance to the engineering team on building scalable ML infrastructure, deployment patterns, and platform capabilities
Develop tooling to streamline model deployment and production workflows, enhancing Data Scientist and AI Engineer productivity
Design and implement scalable ML pipelines, orchestration frameworks, and model serving infrastructure
Collaborate with Data Scientists, AI Engineers, infrastructure, and security teams to integrate AI/ML solutions into production environments
Build and operate secure cloud and on-prem infrastructure leveraging AWS, Kubernetes, SageMaker, Terraform, and related technologies
Support GPU-enabled infrastructure for AI/ML, GenAI, and LLM workloads, ensuring operational efficiency and scalability
Modernize legacy ML pipelines by adopting emerging technologies and best practices
Communicate complex technical concepts, platform capabilities, and architectural decisions to both technical and non-technical stakeholders
Contribute to the development of standards, best practices, and architectural guidelines for the ML platform
Benefits
Comprehensive health insurance including Medical, Dental, and Vision coverage
401(k) retirement savings plan with company matching
Flexible FSA/HSA options for healthcare and dependent care expenses
Life Insurance and Disability coverage
Paid Time Off and holidays to promote work-life balance
Wellness programs and resources to support mental and physical health
Opportunities for professional development and continuous learning
Inclusive and diverse work environment that fosters innovation
Equal Opportunity
Visa is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, or protected veteran status. We are committed to creating an inclusive environment that values diversity and promotes equal opportunity for all employees.

Work arrangement
Yes