Sr. ML Engineer

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, 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.

The Sr. ML Engineeris responsible for building and maintaining ML platform infrastructure that powers AI/ML applications across the organization. This role is suited for a hands-on engineer with practical experience inAWS, Sage Maker, Kubernetes, GPU orchestration, Infrastructure as Code, and MLOps, with a passion for building scalable, secure, and reliable platforms. The position requires an experienced ML platform engineer who can design cloud and on-prem infrastructure, manage model deployment environments, modernize legacy ML pipelines, and enable Data Scientists and AI Engineers to move models from research to production.

The team is tasked with building scalable ML infrastructure and platform tooling, and the successful candidate will contribute to architectural decisions, implementation standards, and best practices across the ML platform.

All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools, Microsoft Copilot, ChatGPT, Git Hub Copilot, and other AI-enabled productivity platforms to support everyday work.

• Lead and deliver specific platform engineering deliverables as a Sr. ML Engineer.
• Provide guidance to the engineering team on building scalable ML infrastructure, deployment patterns, and platform capabilities.
• Improve the productivity of Data Scientists and AI Engineers by developing tooling that simplifies model deployment and productionization.
• Act as a platform design authority and shape best practices and methodologies within the ML platform team.
• Design and build scalable ML pipelines, orchestration frameworks, and model serving infrastructure.
• Collaborate with Data Scientists, AI Engineers, infrastructure teams, and security partners to integrate AI/ML solutions into production systems.
• Build and operate secure cloud and on-prem infrastructure using AWS, Kubernetes, Sage Maker, Terraform, and related platform technologies.
• Support GPU-enabled infrastructure and serving frameworks for AI/ML, GenAI, and LLM workloads.
• Modernize legacy ML pipelines and adopt emerging technologies to improve reliability, scalability, and operational efficiency.
• Communicate technical concepts, platform capabilities, and architectural decisions to technical and non-technical stakeholders.

Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.

• 2+ years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience

• Specialist: 4 or more years of relevant work experience.
• Experience designing, building, and maintaining scalable ML platform infrastructure for AI/ML applications.
• Experience with AWS services such as EC2, S3, EKS, Sage Maker, IAM, VPC, and Cloud Watch.
• Experience managing Kubernetes clusters and containerized ML workloads using Docker.
• Experience with ML pipeline and orchestration tools such as Kubeflow, Airflow, MLflow, or similar platforms.
• Experience building infrastructure automation using Terraform, Cloud Formation, or other Infrastructure as Code tools.
• Experience developing CI/CD pipelines for ML model deployment and infrastructure changes.
• Experience implementing secure cloud architectures using IAM roles, VPCs, least-privilege access, and secure networking patterns.
• Experience with Python and shell scripting for automation, tooling, and platform operations.
• Experience collaborating with Data Scientists, AI Engineers, and cross-functional teams to move models from research to production.
• Experience with generative AI, large language models, LLMOps, or GenAI infrastructure.
• Experience with GPU orchestration for ML training, inference, capacity management, and workload optimization.
• Experience with ML serving frameworks such as vLLM, TensorRT-LLM, KServe, Triton, or similar technologies.
• Experience building and…