Senior ML Ops and Automation Engineer

at  Proofpoint

It’s fun to work in a company where people truly BELIEVE in what they’re doing!
We’re committed to bringing passion and customer focus to the business.
Corporate Overview
Proofpoint is a leading cybersecurity company protecting organizations’ greatest assets and biggest risks: vulnerabilities in people. With an integrated suite of cloud-based solutions, Proofpoint helps companies around the world stop targeted threats, safeguard their data, and make their users more resilient against cyber-attacks. Leading organizations of all sizes, including more than half of the Fortune 1000, rely on Proofpoint for people-centric security and compliance solutions mitigating their most critical risks across email, the cloud, social media, and the web.
We are singularly devoted to helping our customers protect their greatest assets and biggest security risk: their people. That’s why we’re a leader in next-generation cybersecurity.
Role Overview
As an ML Ops and Automation Engineer, you will be at the forefront of bridging the gap between machine learning (ML) development and production deployment, ensuring smooth and efficient operations of ML systems. Your primary focus will be on designing, implementing, and maintaining automated pipelines for model training, deployment, monitoring, and scaling, with the aim of optimizing performance, reliability, and scalability of ML applications. You will collaborate closely with cross-functional teams including data scientists, software engineers, and DevOps to streamline the ML lifecycle and drive innovation in machine learning infrastructure.

Key Responsibilities

• Design and Implement ML Pipelines: Develop end-to-end automation pipelines for ML model training, validation, deployment, and monitoring, integrating with CI/CD systems and version control tools.
• Infrastructure Orchestration: Architect and manage scalable, reliable infrastructure for ML workloads, leveraging cloud services (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes) to ensure high availability and performance.
• Model Versioning and Experiment Tracking: Establish frameworks for versioning ML models and tracking experiment results, enabling reproducibility and collaboration among data scientists and ML engineers.

-Continuous Integration/Continuous Deployment (CI/CD): Implement CI/CD pipelines for automated testing, deployment, and rollback of ML models, ensuring rapid iteration and deployment cycles. (Terraform, AWS cloudformation) -Monitoring and Alerting: Set up robust monitoring and alerting systems to track the performance, health, and drift of deployed ML models in real-time, proactively identifying and addressing issues.

• Optimization and Scaling: Optimize ML workflows for efficiency and cost-effectiveness, and scale infrastructure to accommodate growing data volumes and user loads.
• Security and Compliance: Implement best practices for data security, privacy, and compliance (e.g., GDPR, HIPAA), and ensure adherence to regulatory requirements in ML workflows and deployments.

What you bring to the team:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

-Strong programming skills in languages such as Python, nodejs, or Scala. -Experience with ML frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn).

• Proficiency in cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
• Familiarity with CI/CD tools (e.g., Jenkins, GitLab CI/CD) and version control systems (e.g., Git).
• Solid understanding of DevOps principles and practices.

• Excellent problem-solving and communication skills, with a collaborative mindset.

• Experience with big data technologies (e.g., Apache Spark, Hadoop, hudi, ). - Knowledge of software engineering best practices and agile methodologies. - Understanding of machine learning concepts and techniques.

• Certification in cloud computing or DevOps.

Why Proofpoint
Protecting people is at the heart of our award-winning lineup of cybersecurity solutions, and the people who work here are the key to our success. We’re a customer-focused and a driven-to-win organization with leading-edge products. We are an inclusive, diverse, multinational company that believes in culture fit, but more importantly ‘culture-add’, and we strongly encourage people from all walks of life to apply.
We believe in hiring the best and the brightest to help cultivate our culture of collaboration and appreciation. Apply today and explore your future at Proofpoint! #LifeAtProofpoint
If you like wild growth and working with happy, enthusiastic over-achievers, you’ll enjoy your career with us

• Design and Implement ML Pipelines: Develop end-to-end automation pipelines for ML model training, validation, deployment, and monitoring, integrating with CI/CD systems and version control tools.
• Infrastructure Orchestration: Architect and manage scalable, reliable infrastructure for ML workloads, leveraging cloud services (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes) to ensure high availability and performance.
• Model Versioning and Experiment Tracking: Establish frameworks for versioning ML models and tracking experiment results, enabling reproducibility and collaboration among data scientists and ML engineers