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Palo Alto Networks · 3 months ago

Principal Engineer Machine Learning (MLOps)

Santa Clara, CA

Full-time

Hybrid

Lead/Staff

$165K/yr - $210K/yr

8+ years exp

Palo Alto Networks is dedicated to being the cybersecurity partner of choice, protecting our digital way of life. They are seeking a Principal MLOps Engineer to lead the design and operation of machine learning infrastructure at scale, ensuring reliable and efficient deployments while collaborating with cross-functional teams.

Agentic AICloud SecurityCyber SecurityNetwork SecuritySecurity

Growth Opportunities

No H1B

Responsibilities

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

Qualification

MLOps architectureCloud platformsML infrastructureCI/CD pipelinesProgramming PythonProgramming GoProgramming JavaContainer orchestrationData pipelinesSoft skills

Required

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