Sr Staff ML Engineer - Production & MLOps Focus - GenAI Security Platform (Prisma AIRS, NetSec)

Our Mission At Palo Alto Networks®, we’re united by a shared mission—to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you’re ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you’re in the right place. Who We Are In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values: Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real-world problems and ideating beside the best and the brightest, we invite you to join us! 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 Summary The Team Engineering - The Engineering team is at the core of our products and services. We are a team of innovators, problem-solvers, and builders who are passionate about creating cutting-edge cybersecurity solutions. We work collaboratively to tackle complex challenges, from cloud-native security to threat intelligence and endpoint protection. Our work is critical to protecting our customers' digital way of life. Job Summary Join our team building a cutting-edge multi-tenanted GenAI Security Platform that helps organisations validate and secure their AI systems against adversarial attacks. We're looking for a production-focused ML engineer who can both build ML systems and own their deployment at scale. Key Responsibilities Build and deploy LLM-based agents and multi-step evaluation workflows Fine-tune models, optimize embeddings, and manage model weights and artifacts Deploy and scale ML services on Kubernetes with proper monitoring and resource management Implement experiment tracking, model versioning, and deployment automation Develop observability dashboards for ML metrics, costs, latency, and quality Optimize LLM API usage through caching, batching, and intelligent routing strategies Manage vector database infrastructure and semantic search systems Create CI/CD pipelines for ML artifacts and automated testing frameworks Collaborate with ML researchers to productionize prototypes and scale experiments Qualifications Required Qualifications 4+ years of ML engineering experience with hands-on LLM/NLP work Practical experience building LLM-based applications (agents, multi-turn systems, evaluators) Understanding of model fine-tuning, embedding optimization, and prompt engineering Experience with LLM APIs (OpenAI, Anthropic, AWS Bedrock, Azure OpenAI) Knowledge of LLM orchestration frameworks ( LangChain, LlamaIndex, Pydantic AI, custom solutions) Familiarity with model architectures and when to fine-tune vs prompt engineer Strong experience deploying ML models to production at scale Experience with Model serving frameworks (vLLM preferred; TensorRT-LLM, Ray Serve, or similar a plus) Kubernetes and Docker proficiency for ML workload orchestration Hands-on experience with ML experiment tracking and model versioning tools Understanding of CI/CD for ML systems with automated testing and validation Knowledge of distributed computing, async processing, and job queues Experience with monitoring, observability, and cost optimisation for ML systems Proficiency with cloud platforms (GCP preferred, AWS/Azure acceptable) Experience managing vector databases and similarity search at scale Understanding of caching strategies (Redis) and data pipeline architectures Knowledge of infrastructure-as-code and GitOps workflows Expert Python skill ... (truncated, view full listing at source)