> Markdown version of [/jobs/ext/1955064-sr-ml-engineer](https://www.wearedevelopers.com/jobs/ext/1955064-sr-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. ML Engineer - **Company:** Visa Inc. - **Location:** United States - **Experience:** Expert - **Salary:** $130,700.0 - $202,300.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Cloud Computing Security, Cloud Engineering, Computer Literacy, Continuous Integration, Monitoring of Systems, Identity and Access Management, Information Systems Security Architecture Professional, Machine Learning, Productivity Software, Role-Based Access Control, Prometheus, Azure Machine Learning, AI Infrastructure, Data Logging, Cloud Platform System, GitHub Copilot, Delivery Pipeline, Large Language Models, Grafana, Generative AI, Infrastructure as Code (IaC), Cloudformation, Kubernetes, Infrastructure Automation Frameworks, Machine Learning Operations, TensorRT, Cloudwatch, Terraform, GPT - **Published:** August 6, 2026 - **Apply:** https://www.dice.com/job-detail/f8c8acee-8693-4666-803c-014dbb51676a ## About the Role All roles require digital fluency, including the ability to work with emerging technologies and AI-assisted tools - such as AI coding assistants (e.g., GitHub Copilot, ChatGPT, Claude Code, CLine), advanced reasoning GenAI models, and enterprise productivity tools - to enhance engineering productivity and support everyday work., * 2+ years of relevant work experience and a Bachelor's degree, OR 5+ years of relevant work experience. * Experience in developing and implementing scalable AI/ML models and algorithms. * Experience managing Kubernetes clusters and Kubeflow for ML pipelines, * 3 or more years of work experience with a Bachelor's Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD). * Experience in building ML serving infrastructure (vLLM, TensorRT-LLM, KServe, Triton). * Experience in implementing secure architectures (IAM, VPCs, least privilege). * Experience with automating infrastructure with Terraform/CloudFormation. * Experience with developing CI/CD pipelines for ML model deployment. * Experience with implement monitoring tools (CloudWatch, Prometheus, Grafana). * Cloud-agnostic experience welcomed ## Description The Sr. ML Engineer is responsible for designing, building, and managing the scalable cloud infrastructure that powers our AI and Machine Learning applications. Rather than focusing primarily on model building, this role is suited for a specialist with deep expertise in MLOps, AWS cloud architecture, Kubernetes, and system design. You will own key modules of the ML platform, perform architectural reviews, and implement robust deployment standards. The team is tasked with building secure, scalable pipelines and serving infrastructure for both traditional ML and modern Generative AI (LLM) workloads. The successful candidate will act as a design authority for model deployment, infrastructure automation, and platform security, shaping best practices to enable our data scientists and AI engineers to seamlessly transition models from research to production., * ML Platform Architecture: Design, build, and maintain scalable, highly available Machine Learning infrastructure on AWS and Visa OnPrem. * Kubernetes & Kubeflow Management: Deploy, configure, and manage Kubernetes clusters and Kubeflow to orchestrate complex ML training and deployment pipelines. * Model Deployment & LLMOps: Build robust serving infrastructure to productionize machine learning models and Large Language Models (LLMs) using modern serving frameworks (e.g., vLLM, TensorRT-LLM, KServe, Triton). * Cloud Security & Access Management: Design secure platform architectures utilizing AWS IAM (roles, policies, least privilege), VPCs, and security groups to ensure data and model security. * System Design & Infrastructure as Code (IaC): Architect scalable cloud systems and automate infrastructure provisioning using tools like Terraform or AWS CloudFormation. * CI/CD & MLOps: Develop and maintain automated CI/CD pipelines for model training, testing, and deployment, ensuring seamless continuous integration. * Cross-Functional Enablement: Partner closely with Data Scientists and AI Engineers to understand their compute and tooling needs, reducing friction in the model development lifecycle. * Observability & Monitoring: Implement logging, monitoring, and alerting for ML models and underlying infrastructure (e.g., CloudWatch, Prometheus, Grafana) to track system health, model drift, and latency. * Modernization: Act as a technical guide to modernize legacy deployment pipelines and integrate emerging AI infrastructure technologies. This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts)