ML Engineer, Sr.
Wiraa · United States · 3 wk ago
RemoteRemoteInformation TechnologyFull-time
We need to output HTML fragment with sections: About Us? Actually "About Us" is part of description but not a heading? The instruction: use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we should include headings for sections that have content: About the role, Responsibilities, Qualifications, Benefits. Possibly also "About Us"? Not listed as allowed heading; but we could treat as a paragraph. The instruction: "Do NOT repeat the job title as a heading (the page already shows it)". So we can include About Us as a paragraph under maybe no heading. But we could also include an
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we should include headings for sections that have content: About the role, Responsibilities, Qualifications, Benefits. Possibly also "About Us"? Not listed as allowed heading; but we could treat as a paragraph. The instruction: "Do NOT repeat the job title as a heading (the page already shows it)". So we can include About Us as a paragraph under maybe no heading. But we could also include an About Us? Not in list; but they said e.g., not exclusive. Safer to follow allowed headings. We'll include About the role, Responsibilities, Qualifications, Benefits. Also maybe "Skills"? Not present. We'll embed content accordingly.
We need to preserve all substantive information, remove boilerplate: navigation, legal, EEO, equal-opportunity, scam-warning, duplicated lines, application instructions. The posting includes "Equal Opportunity Visa is an equal opportunity employer..." That's EEO boilerplate; remove. Also "About Us" paragraph is substantive? It's about company; we can keep as a paragraph without heading.
We need to use - for any list of duties/requirements/benefits. So responsibilities list: we have bullet-like sentences; we can convert to
- . Qualifications list similarly. Benefits list similarly.
We must not create Pay/Schedule/Benefits sections unless posting actually states them. Benefits are stated, so we include Benefits section.
We must not include filler like "commensurate with experience". Not present.
We must output only HTML fragment, no /, no markdown fences.
Let's extract content.
About Us paragraph: "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."
We'll put that as
About Us ...
.
About The Role heading: About the role
then paragraph: "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."
We'll put that as ...
.
Responsibilities: list bullet points from "Lead and deliver specific platform engineering initiatives as a Senior ML Engineer" etc. We'll extract each sentence starting with a verb.
Responsibilities list:
- 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
We'll put under Responsibilities
then - ...
.
Qualifications: list bullet points from qualifications section.
Qualifications list:
- 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
We'll put under Qualifications
then - ...
.
Benefits: list bullet points.
Benefits list:
- 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
We'll put under Benefits
then - ...
.
We should not include any other sections like Skills, Pay, Schedule.
We need to ensure we preserve all substantive information; we have included About Us paragraph, About the role paragraph, responsibilities, qualifications, benefits.
Check for any duplicated lines: we have not repeated.
Now output HTML fragment.
Let's craft:
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.
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
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
- for any list of duties/requirements/benefits. So responsibilities list: we have bullet-like sentences; we can convert to
- . Qualifications list similarly. Benefits list similarly.
We must not create Pay/Schedule/Benefits sections unless posting actually states them. Benefits are stated, so we include Benefits section.
We must not include filler like "commensurate with experience". Not present.
We must output only HTML fragment, no /, no markdown fences.
Let's extract content.
About Us paragraph: "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."
We'll put that as
About Us ...
. About The Role heading:About the role
then paragraph: "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." We'll put that as...
. Responsibilities: list bullet points from "Lead and deliver specific platform engineering initiatives as a Senior ML Engineer" etc. We'll extract each sentence starting with a verb. Responsibilities list: - 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 We'll put underResponsibilities
then- ...
Qualifications
then- ...
Benefits
then- ...
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.
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
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
- . Qualifications list similarly. Benefits list similarly.
We must not create Pay/Schedule/Benefits sections unless posting actually states them. Benefits are stated, so we include Benefits section.
We must not include filler like "commensurate with experience". Not present.
We must output only HTML fragment, no /, no markdown fences.
Let's extract content.
About Us paragraph: "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."
We'll put that as