Staff machine learning engineer

3 weeks ago

Kannur, India Zscaler Full time

At Zscaler, our AI and Data Science teams are at the forefront of cybersecurity, tackling critical use cases like threat detection, policy recommendation, malware detection, content classification, and AIOps. As a Staff Machine Learning Engineer, you will be a technical leader responsible for architecting and implementing robust, scalable machine learning systems that power our cloud security platform. You will be a key influencer of our technical direction, bridging the gap between innovative research and production-grade engineering. You'll own complex projects end-to-end, mentor other engineers, and set the standard for technical excellence within the team. Your work will involve not only developing advanced models but also building the distributed systems and operational frameworks required to run them at scale. Responsibilities Design and deploy the end-to-end lifecycle of production-grade Gen AI/ML systems, from data ingestion to deployment and monitoring, ensuring scalability, reliability, and efficiency. Drive innovation by researching and evaluating emerging AI/ML frameworks, rapidly prototyping novel solutions to validate their feasibility and championing full-scale implementation. Implement and maintain robust MLOps practices, including logging, monitoring, and CI/CD pipelines for distributed ML systems. Provide technical leadership and mentor junior engineers on best practices in system design and architecture, fostering a culture of technical excellence. Collaborate with cross-functional teams to translate business needs into technical solutions. Required Skills 5+ years of experience as an MLE, with a track record of shipping complex, scalable ML systems to production. Proven experience in creating Gen AI/ML-based systems using Large Language Models (LLMs), fine-tuning LLMs for specific tasks, Retrieval-Augmented Generation (RAG) and Agentic AI and operationalizing them in production environments. Proven experience designing and implementing distributed ML systems with deep knowledge of MLOps (monitoring, logging, ops). A solid computer science foundation (data structures, algorithms, system design) coupled with expertise in Python (scikit-learn, Py Torch/Tensor Flow) and SQL for comprehensive feature engineering, model evaluation, and error analysis. Excellent communication and interpersonal skills. Preferred Skills Expertise with cloud services (AWS, GCP, Azure) and ML platforms (Kubeflow, Sage Maker). Proficiency in systems programming languages like Go or Rust, with a deep understanding of distributed systems, OS, and networking fundamentals. A record of research, publications, or patents in AI/ML.

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