Principal Engineer MLOps (DLP Detection) at Palo Alto Networks

Santa Clara, California, United States -

Full Time

Start Date

Immediate

Expiry Date

29 Dec, 25

Salary

210000.0

Posted On

30 Sep, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

MLOps, Machine Learning, DevOps, Cloud Systems, Python, Go, Java, AWS, GCP, Azure, Kubernetes, Docker, CI/CD, Terraform, Monitoring, Data Pipelines

Industry

Computer and Network Security

Description

Company Description Our Mission At Palo Alto Networks® everything starts and ends with our mission: Being the cybersecurity partner of choice, protecting our digital way of life. Our vision is a world where each day is safer and more secure than the one before. We are a company built on the foundation of challenging and disrupting the way things are done, and we’re looking for innovators who are as committed to shaping the future of cybersecurity as we are. Who We Are We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes. Job Description Your Career We are looking for a Principal MLOps Engineer to lead the design, development, and operation of production-grade machine learning infrastructure at scale. In this role, you will architect robust pipelines, deploy and monitor ML models, and ensure reliability, reproducibility, and governance across our AI/ML ecosystem. You will work at the intersection of ML, DevOps, and cloud systems, enabling our teams to accelerate experimentation while ensuring secure, efficient, and compliant deployments. This role is located at our dynamic Santa Clara California headquarters campus, and in office 3 days a week. Not a remote role. Your Impact Lead MLOps architecture: Design and implement scalable ML platforms, CI/CD pipelines, and deployment workflows across cloud and hybrid environments Operationalize ML models: Build automated systems for training, testing, deployment, monitoring, and rollback of ML models in production Ensure reliability and governance: Implement model versioning, reproducibility, auditing, and compliance best practices Drive observability & monitoring: Develop real-time monitoring, alerting, and logging solutions for ML services, ensuring performance, drift detection, and system health Champion automation & efficiency: Reduce friction between data science, engineering, and operations by introducing best practices for infrastructure-as-code, container orchestration, and continuous delivery Collaborate cross-functionally: Partner with ML engineers, data scientists, security teams, and product engineering to deliver robust, production-ready AI systems Lead innovation in MLOps: Evaluate and introduce new tools, frameworks, and practices that elevate the scalability, reliability, and security of ML operations Optimize ML infrastructure for cost efficiency and reduced bootstrapping times across various environments. Qualifications Your Experience MS / PhD in Computer Science, Engineering, or related field, or equivalent military/industry experience 8+ years of software/DevOps/ML engineering experience, with at least 3+ years focused on MLOps or ML platform engineering Strong programming skills (Python, Go, or Java) with deep expertise in building production systems Experience with cloud platforms (AWS, GCP, Azure) and container orchestration (Kubernetes, Docker) Proven experience in ML infrastructure: model serving (TensorFlow Serving, TorchServe, Triton), workflow orchestration (Airflow, Kubeflow, MLflow, Ray, Vertex AI, SageMaker) Hands-on experience with CI/CD pipelines, infrastructure-as-code (Terraform, Helm), and monitoring/observability tools (Prometheus, Grafana, ELK/EFK stack) Strong knowledge of data pipelines, feature stores, and streaming systems (Kafka, Spark, Flink) Understanding of model monitoring, drift detection, retraining pipelines, and governance frameworks Ability to influence cross-functional stakeholders, define best practices, and mentor engineers at all levels Passion for operational excellence, scalability, and securing ML systems in mission-critical environments Additional Information The Team Our engineering team is at the core of our products and connected directly to the mission of preventing cyberattacks. We are constantly innovating — challenging the way we, and the industry, think about cybersecurity. Our engineers don’t shy away from building products to solve the problems no one has pursued before. We define the industry instead of waiting for directions. We need individuals who feel comfortable in ambiguity, excited by the prospect of a challenge, and empowered by the unknown risks facing our everyday lives that are only enabled by a secure digital environment. Compensation Disclosure The compensation offered for this position will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non-sales roles) or base salary + commission target (for sales/commissioned roles) is expected to be between - $165000 - $210000/YR. The offered compensation may also include restricted stock units and a bonus. A description of our employee benefits may be found here. Our Commitment We’re problem solvers that take risks and challenge cybersecurity’s status quo. It’s simple: we can’t accomplish our mission without diverse teams innovating, together. We are committed to providing reasonable accommodations for all qualified individuals with a disability. If you require assistance or accommodation due to a disability or special need, please contact us at [email protected]. Palo Alto Networks is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics. All your information will be kept confidential according to EEO guidelines. Is role eligible for Immigration Sponsorship? No. Please note that we will not sponsor applicants for work visas for this position.

Responsibilities

Lead the design, development, and operation of production-grade machine learning infrastructure. Ensure reliability, reproducibility, and governance across the AI/ML ecosystem.