Applied Materials, Inc.
MLOps Engineer
Applied Materials, Inc., Santa Clara, California, us, 95053
time left to apply End Date: January 7, 2025 (4 days left to apply)
job requisition id R2420559
Applied Materials’ IT organization has a long reputation of being a great place to work. The IT team has been recognized as one of Computerworld's 100 Best Places to Work in IT nine times.
Role Overview: As an MLOps Engineer, you will be responsible for ensuring the smooth operation of ML pipelines, from model development to deployment and monitoring. You will work across the full lifecycle of ML systems, including CI/CD, model versioning, orchestration, performance tuning, and automation.
Key Responsibilities:
Act as a liaison with a sub-group within a business unit or a GIS Domain area for business and MLOps technology strategy alignment, solution discovery, service management, and project portfolio management.
Analyze Business Requirements:
Convert business requirements and/or issues into functional and technical specifications.
Assist in the design and technical development of complex MLOps solutions to meet business needs.
Perform and document application and platform configuration.
Prepare and execute test scenarios and scripts (unit, integration, performance, regression, acceptance) and data integration.
Participate in new technology evaluations.
Adhere to GIS Processes:
Guide junior staff and contingent workers to adhere to GIS project management, software application development, testing, service management, change management, RCA, and other relevant processes, standards, governance, and controls.
Manage the execution of SOX controls and testing, and support internal and external audits.
Plan and Manage MLOps Projects:
Plan and manage small to medium-sized MLOps projects to ensure effective and efficient execution in line with guardrails of scope, timeline, budget, and quality.
Serve as an MLOps team lead on medium to large cross-functional application processes.
Model Deployment & Orchestration:
Collaborate with Data Scientists to deploy machine learning models into production environments.
Design and implement CI/CD pipelines to automate the training, validation, and deployment of models.
Ensure seamless integration of models with backend systems and cloud infrastructure.
Infrastructure Management:
Build and maintain scalable infrastructure for ML workflows using cloud platforms (AWS/GCP/Azure).
Manage containerized environments (Docker, Kubernetes) for model deployment and scaling.
Optimize model serving environments for low-latency and high-availability needs.
Monitoring & Optimization:
Implement and maintain monitoring and logging systems to track model performance and identify issues in real-time.
Ensure model performance is aligned with business goals and continually improve model retraining cycles.
Implement auto-scaling and fault-tolerant mechanisms to ensure high availability of ML services in production.
Automation & Tooling:
Develop scripts and tools to automate repetitive tasks within the ML lifecycle (data collection, preprocessing, retraining).
Security and Compliance:
Implement security best practices for data privacy, access control, and model integrity in production environments.
Ensure compliance with relevant industry regulations for ML operations.
Qualifications and Experience:
Technical Skills:
Proficient in Python or other scripting languages.
Experience with ML model deployment frameworks such as TensorFlow Serving or custom REST APIs.
Strong knowledge of containerization technologies (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure).
Familiarity with CI/CD tools and version control systems (Git).
Understanding of ML lifecycle tools (Kubeflow, MLflow) is a plus.
Experience in monitoring, logging, and alerting tools (Prometheus, Grafana, Datadog).
Experience:
4+ years of experience working in MLOps, DevOps, or related roles in a production environment.
Demonstrated experience deploying and maintaining machine learning models at scale in production.
Knowledge of model performance monitoring, A/B testing, and model retraining strategies.
Education:
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.
Relevant certifications (e.g., AWS Certified Machine Learning, Google Cloud Professional Machine Learning Engineer) are a plus.
Additional Information Time Type:
Full time
Employee Type:
Assignee / Regular
Travel:
Yes, 10% of the Time
Relocation Eligible:
Yes
U.S. Salary Range:
$152,000.00 - $209,000.00
Applied Materials is an Equal Opportunity Employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.
About Us Applied Materials is the leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. Our expertise in modifying materials at atomic levels and on an industrial scale enables customers to transform possibilities into reality. At Applied Materials, our innovations Make Possible a Better Future.
#J-18808-Ljbffr
job requisition id R2420559
Applied Materials’ IT organization has a long reputation of being a great place to work. The IT team has been recognized as one of Computerworld's 100 Best Places to Work in IT nine times.
Role Overview: As an MLOps Engineer, you will be responsible for ensuring the smooth operation of ML pipelines, from model development to deployment and monitoring. You will work across the full lifecycle of ML systems, including CI/CD, model versioning, orchestration, performance tuning, and automation.
Key Responsibilities:
Act as a liaison with a sub-group within a business unit or a GIS Domain area for business and MLOps technology strategy alignment, solution discovery, service management, and project portfolio management.
Analyze Business Requirements:
Convert business requirements and/or issues into functional and technical specifications.
Assist in the design and technical development of complex MLOps solutions to meet business needs.
Perform and document application and platform configuration.
Prepare and execute test scenarios and scripts (unit, integration, performance, regression, acceptance) and data integration.
Participate in new technology evaluations.
Adhere to GIS Processes:
Guide junior staff and contingent workers to adhere to GIS project management, software application development, testing, service management, change management, RCA, and other relevant processes, standards, governance, and controls.
Manage the execution of SOX controls and testing, and support internal and external audits.
Plan and Manage MLOps Projects:
Plan and manage small to medium-sized MLOps projects to ensure effective and efficient execution in line with guardrails of scope, timeline, budget, and quality.
Serve as an MLOps team lead on medium to large cross-functional application processes.
Model Deployment & Orchestration:
Collaborate with Data Scientists to deploy machine learning models into production environments.
Design and implement CI/CD pipelines to automate the training, validation, and deployment of models.
Ensure seamless integration of models with backend systems and cloud infrastructure.
Infrastructure Management:
Build and maintain scalable infrastructure for ML workflows using cloud platforms (AWS/GCP/Azure).
Manage containerized environments (Docker, Kubernetes) for model deployment and scaling.
Optimize model serving environments for low-latency and high-availability needs.
Monitoring & Optimization:
Implement and maintain monitoring and logging systems to track model performance and identify issues in real-time.
Ensure model performance is aligned with business goals and continually improve model retraining cycles.
Implement auto-scaling and fault-tolerant mechanisms to ensure high availability of ML services in production.
Automation & Tooling:
Develop scripts and tools to automate repetitive tasks within the ML lifecycle (data collection, preprocessing, retraining).
Security and Compliance:
Implement security best practices for data privacy, access control, and model integrity in production environments.
Ensure compliance with relevant industry regulations for ML operations.
Qualifications and Experience:
Technical Skills:
Proficient in Python or other scripting languages.
Experience with ML model deployment frameworks such as TensorFlow Serving or custom REST APIs.
Strong knowledge of containerization technologies (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure).
Familiarity with CI/CD tools and version control systems (Git).
Understanding of ML lifecycle tools (Kubeflow, MLflow) is a plus.
Experience in monitoring, logging, and alerting tools (Prometheus, Grafana, Datadog).
Experience:
4+ years of experience working in MLOps, DevOps, or related roles in a production environment.
Demonstrated experience deploying and maintaining machine learning models at scale in production.
Knowledge of model performance monitoring, A/B testing, and model retraining strategies.
Education:
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.
Relevant certifications (e.g., AWS Certified Machine Learning, Google Cloud Professional Machine Learning Engineer) are a plus.
Additional Information Time Type:
Full time
Employee Type:
Assignee / Regular
Travel:
Yes, 10% of the Time
Relocation Eligible:
Yes
U.S. Salary Range:
$152,000.00 - $209,000.00
Applied Materials is an Equal Opportunity Employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.
About Us Applied Materials is the leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. Our expertise in modifying materials at atomic levels and on an industrial scale enables customers to transform possibilities into reality. At Applied Materials, our innovations Make Possible a Better Future.
#J-18808-Ljbffr