Job description

"Please note, this role is not able to offer visa transfer or sponsorship now or in the future." About the role As a Senior MLOps Engineer , you will make an impact by designing, implementing, and managing the cloud infrastructure, deployment pipelines, and monitoring frameworks that enable machine learning models to run reliably and efficiently in production environments. You will be a valued member of the AI & Data Engineering team and work collaboratively with Data Scientists, Data Engineers, business stakeholders, and global delivery teams to establish and scale MLOps capabilities on Google Cloud Platform (GCP). In this role, you will: • Lead the migration, containerization, and deployment of existing machine learning models into the Google Cloud Vertex AI ecosystem. • Design, implement, and maintain scalable CI/CD/CT pipelines using Vertex AI Pipelines, Kubeflow, Cloud Build, and related cloud-native technologies. • Develop and manage model deployment frameworks supporting both real-time and batch inference workloads while ensuring reliability, scalability, and performance. • Establish monitoring, governance, and lifecycle management processes, including model drift detection, version control, model registry management, and rollback strategies. • Partner with business stakeholders, Data Science teams, and Data Engineering teams to define requirements, align priorities, and deliver enterprise-grade MLOps solutions. Work model We believe hybrid work is the way forward as we strive to provide flexibility wherever possible. Based on this role's business requirements, this is a hybrid position requiring regular onsite presence in Atlanta, Georgia . Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programs. The working arrangements for this role are accurate as of the date of posting. This may change based on the project you're engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations. What you need to have to be considered • 7+ years of experience in Data Engineering, Software Engineering, Machine Learning Engineering, or a related technical field. • 3+ years of hands-on experience building and managing MLOps solutions and deploying machine learning models into production environments. • Strong expertise with Google Cloud Platform services, including Vertex AI, BigQuery, Cloud Storage, Pub/Sub, and related cloud-native technologies. • Advanced programming experience with Python and SQL, along with hands-on expertise in Docker, Kubernetes, and Google Kubernetes Engine (GKE). • Proven experience implementing CI/CD pipelines and Infrastructure as Code using tools such as Terraform, GitHub Actions, GitLab CI, Jenkins, or Cloud Build. • Demonstrated ability to lead technical initiatives, collaborate across distributed teams, and communicate effectively with both technical and non-technical stakeholders. These will help you stand out • Google Cloud Professional Machine Learning Engineer or Professional Cloud Architect certification. • Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-Learn. • Experience implementing model monitoring, governance, and compliance frameworks in enterprise environments. • Familiarity with Agile and Scrum methodologies. • Experience mentoring engineers and leading offshore or distributed delivery teams. We're excited to meet people who share our mission and can make an impact in a variety of ways. Don't hesitate to apply, even if you only meet the minimum requirements listed. Think about your transferable experiences and unique skills that make you stand out as someone who can bring new and exciting things to this role. Applications will be accepted until September 29th, 2026. Salary and Other Compensation : The annual salary for this position is between $130,000 - $155,000 depending on experience and other qualifications of the successful candidate. This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans. Benefits : Cognizant offers the following benefits for this position, subject to applicable eligibility requirements: • Medical/Dental/Vision/Life Insurance • Paid holidays plus Paid Time Off • 401(k) plan and contributions • Long-term/Short-term Disability • Paid Parental Leave • Employee Stock Purchase Plan Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law. About Cognizant: Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant. Additional employment information Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law. Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview. Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws. If you have a disability that requires reasonable accommodation to search for a job opening or submit an application, please email CareersNA2@cognizant.com for roles based in the Americas or CareersIndia2@cognizant.com for roles based in India.

Requirements

Must-haves

"Please note, this role is not able to offer visa transfer or sponsorship now or in the future."
Lead the migration, containerization, and deployment of existing machine learning models into the Google Cloud Vertex AI ecosystem.
Design, implement, and maintain scalable CI/CD/CT pipelines using Vertex AI Pipelines, Kubeflow, Cloud Build, and related cloud-native technologies.
Develop and manage model deployment frameworks supporting both real-time and batch inference workloads while ensuring reliability, scalability, and performance.
Establish monitoring, governance, and lifecycle management processes, including model drift detection, version control, model registry management, and rollback strategies.
Partner with business stakeholders, Data Science teams, and Data Engineering teams to define requirements, align priorities, and deliver enterprise-grade MLOps solutions.
What you need to have to be considered
7+ years of experience in Data Engineering, Software Engineering, Machine Learning Engineering, or a related technical field.
3+ years of hands-on experience building and managing MLOps solutions and deploying machine learning models into production environments.
Strong expertise with Google Cloud Platform services, including Vertex AI, BigQuery, Cloud Storage, Pub/Sub, and related cloud-native technologies.
Advanced programming experience with Python and SQL, along with hands-on expertise in Docker, Kubernetes, and Google Kubernetes Engine (GKE).
Proven experience implementing CI/CD pipelines and Infrastructure as Code using tools such as Terraform, GitHub Actions, GitLab CI, Jenkins, or Cloud Build.
Demonstrated ability to lead technical initiatives, collaborate across distributed teams, and communicate effectively with both technical and non-technical stakeholders.
These will help you stand out
Google Cloud Professional Machine Learning Engineer or Professional Cloud Architect certification.
Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-Learn.
Experience implementing model monitoring, governance, and compliance frameworks in enterprise environments.
Familiarity with Agile and Scrum methodologies.
Experience mentoring engineers and leading offshore or distributed delivery teams.
Applications will be accepted until September 29th, 2026.
The annual salary for this position is between $130,000 - $155,000 depending on experience and other qualifications of the successful candidate.
Benefits : Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
Long-term/Short-term Disability
Employee Stock Purchase Plan

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaigcpci/cdrestpythonsqldockerkubernetesagilescrum

Platform: cognizant
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off

Parental Leave