ML Engineer- Sr Consultant

Engg; Austin (TX); On-site; USD 163,000 - 262,000; Full time

Job summary

Visa seeks an experienced Machine Learning Engineer at senior levels to productionize models, optimize inference, and build robust pipelines in a cloud environment. You will collaborate with data scientists, product teams, and platform teams to translate research into scalable software with strong deployment practices.

Expect emphasis on security, compliance, and extensive MLOps capabilities. You will work in an environment that embraces generative AI tools and modern ML platforms while

Qualifications

• 8+ years of relevant work experience with a Bachelor Degree or at least 5 years with an Advanced Degree or 2 years with a PhD.
• Bachelor’s Degree in Computer Science, Engineering, ML, Statistics, OR Operations Research, or related field.
• Experience deploying, operationalizing, or supporting machine learning models in production environments.
• Strong programming experience in Python, Java, Scala, C++, C#, Go, or Rust.

Responsibilities

• Deploy and productionize machine learning models.
• Build and maintain model deployment pipelines including packaging, testing, versioning, release management, and rollback.
• Optimize models and inference services for latency, throughput, scalability, reliability, and cost.
• Support batch, streaming, real-time, and API-based model serving environments.
• Partner with data scientists to understand model logic, features, and validation metrics.
• Translate model artifacts into production-ready code and services.
• Implement monitoring for model performance, data quality, drift, and availability.
• Support A/B testing, champion/challenger, and controlled rollout strategies.
• Contribute to MLOps: CI/CD, registries, feature stores, orchestration, observability.
• Troubleshoot production issues in model serving and data pipelines.
• Ensure security, compliance, explainability, auditability, and reliability.

Skills

Python

Java

Scala

C++

C#

Go

Rust

MLOps

CI/CD

Docker

Kubernetes

Cloud platforms

SageMaker

Vertex AI

Databricks

TensorFlow

PyTorch

scikit-learn

XGBoost

Education

Bachelor’s Degree in Computer Science, Engineering, or related quantitative field

Advanced Degree (Masters, MBA, JD, MD)

PhD (2 years experience)

Tools

MLflow

Kubeflow

SageMaker

Vertex AI

Databricks

TensorFlow

PyTorch

scikit-learn

XGBoost

Job description

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.

Job Description

As a Machine Learning Engineer at the Senior Consultant/Senior Manager level at VISA, you will be responsible for deploying, optimizing, and maintaining machine learning models in production environments. You will work closely with data scientists, engineers, product teams, and platform teams to take models from experimentation into scalable, reliable, and secure production systems. This role requires a strong understanding of how machine learning models are built, trained, validated, and evaluated; however, the primary focus is not model research or development. Instead, the role is centered on productionizing models, optimizing inference performance, building deployment pipelines, monitoring model behavior, and ensuring long-term operational reliability. You will translate model artifacts and technical requirements into production-grade software, services, and pipelines using modern programming languages, cloud platforms, and MLOps practices. You will help ensure models are performant, explainable where required, well-monitored, and aligned with enterprise standards for security, compliance, and reliability. All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools - for example, ChatGPT, Microsoft Copilot, and similar tools - to support everyday work.

Key Responsibilities

• Deploy and productionize machine learning models developed by data science teams.
• Build and maintain model deployment pipelines, including packaging, testing, versioning, release management, and rollback processes.
• Optimize models and inference services for latency, throughput, scalability, reliability, cost, and resource efficiency.
• Support batch, streaming, real-time, and API-based model serving environments.
• Partner with data scientists to understand model logic, features, dependencies, validation metrics, and expected production behavior.
• Translate model artifacts and technical specifications into production-ready code and services.
• Implement monitoring for model performance, data quality, feature drift, model drift, latency, availability, and prediction quality.
• Support model validation, A/B testing, champion/challenger testing, and controlled rollout strategies.
• Contribute to MLOps capabilities such as CI/CD, model registries, feature stores, orchestration, observability, and automated testing.
• Troubleshoot production issues related to model serving, data pipelines, infrastructure, and performance.
• Ensure production ML solutions meet requirements for security, compliance, explainability, auditability, and operational resilience.

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

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

Qualifications

Basic Qualifications

• 8 or more years of relevant work experience with a Bachelor Degree or at least 5 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 2 years of work experience with a PhD.
• Bachelor’s Degree in Computer Science, Engineering, Machine Learning, Statistics, Operations Research, Mathematics, or a related quantitative field, or equivalent experience.
• Experience deploying, operationalizing, or supporting machine learning models in production environments.
• Strong programming experience in one or more languages such as Python, Java, Scala, C++, C#, Go, or Rust.
• Understanding of machine learning concepts, including model training, feature engineering, validation, evaluation, and performance metrics.
• Experience building production software, APIs, data pipelines, or distributed systems.
• Experience with cloud platforms, containerized applications, or scalable data/ML infrastructure.
• Advanced Generative AI experience or usage.
• Strong experience with MLOps, model deployment, model serving, model monitoring, and production ML systems.
• Experience optimizing ML models or inference pipelines for latency, throughput, cost, scalability, and reliability.
• Experience with tools and platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, Databricks, TensorFlow, PyTorch, scikit-learn, or XGBoost.
• Experience with Docker, Kubernetes, CI/CD pipelines, cloud platforms, and observability tools.
• Experience with batch scoring, real-time inference APIs, streaming pipelines, or feature pipelines.
• Experience monitoring for data drift, model drift, prediction quality, availability, and operational SLAs.
• Experience with A/B testing, canary deployments, blue/green deployments, or champion/challenger model frameworks.
• Experience working with large datasets and big data technologies such as Spark, Kafka, Snowflake, Hadoop, Hive, or Databricks.
• Familiarity with modeling techniques such as logistic regression, decision trees, gradient boosting, neural networks, SVM, Naïve Bayes, or Bayesian methods.
• Experience with explainability, governance, auditability, compliance, and post-deployment model integrity.

Preferred Qualifications

• 9 or more years of relevant work experience with a Bachelor’s Degree, or 7 or more years of experience with an Advanced Degree, or 3 or more years of experience with a PhD.
• Bachelor’s Degree in Computer Science, Engineering, Machine Learning, Statistics, Operations Research, Mathematics, or a related quantitative field, or equivalent experience.
• Experience deploying, operationalizing, or supporting machine learning models in production environments.
• Strong programming experience in one or more languages such as Python, Java, Scala, C++, C#, Go, or Rust.
• Understanding of machine learning concepts, including model training, feature engineering, validation, evaluation, and performance metrics.
• Experience building production software, APIs, data pipelines, or distributed systems.
• Experience with cloud platforms, containerized applications, or scalable data/ML infrastructure.
• Advanced Generative AI experience or usage.
• Strong experience with MLOps, model deployment, model serving, model monitoring, and production ML systems.
• Experience optimizing ML models or inference pipelines for latency, throughput, cost, scalability, and reliability.
• Experience with tools and platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, Databricks, TensorFlow, PyTorch, scikit-learn, or XGBoost.
• Experience with Docker, Kubernetes, CI/CD pipelines, cloud platforms, and observability tools.
• Experience with batch scoring, real-time inference APIs, streaming pipelines, or feature pipelines.
• Experience monitoring for data drift, model drift, prediction quality, availability, and operational SLAs.
• Experience with A/B testing, canary deployments, blue/green deployments, or champion/challenger model frameworks.
• Experience working with large datasets and big data technologies such as Spark, Kafka, Snowflake, Hadoop, Hive, or Databricks.
• Familiarity with modeling techniques such as logistic regression, decision trees, gradient boosting, neural networks, SVM, Naïve Bayes, or Bayesian methods.
• Experience with explainability, governance, auditability, compliance, and post-deployment model integrity.

Information for US Applicants

For roles located in the US, the estimated salary range for this position is $163,400.00 to $ 261,500.00 USD per year, which may include potential sales incentive payments (if applic