Sr Staff ML Engineer - Production & MLOps Focus - GenAI Security Platform Job at Palo Alto Networks (Bengaluru)

Job Description

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.

Job Responsibility

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

Requirements

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 skills (async/await, type hints, Pydantic, testing)
Experience with ML frameworks (PyTorch/TensorFlow helpful but not required)
SQL proficiency for analytics and data pipeline development
Strong software engineering practices (testing, code review, documentation)

Nice to have

Experience with model training, LoRA, PEFT, or custom fine-tuning pipelines
Background in building multi-agent systems or complex LLM workflows
Knowledge of AI safety, adversarial ML, or security testing
Previous work optimizing LLM costs and latency at scale
Familiarity with graph databases or relationship modeling
Experience in high-scale production ML environments

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