Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange™️ platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 160+ public exchanges globally and thousands of private exchanges at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform.

We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler.

Role

We are looking for a Senior Machine Learning Engineer to join our Exposure Management & Security Operations team. This role is a hybrid position based in Bangalore, reporting to the Manager, Machine Learning Engineering. You will join the team that built the world’s largest cloud security platform from the ground up, helping to scale a multitenant architecture that serves over 15 million users globally. Your vision and passion will be critical as we continue to innovate and enable organizations to harness the speed and agility of a cloud-first strategy.

What you’ll do (Role Expectations)

Independently develop and implement machine learning models and Generative AI solutions, with a strong emphasis on building and deploying Agentic AI architectures
Drive the end-to-end productionization of AI agents, navigating the complexities of multi-step reasoning, tool use, state management, and LLM orchestration in live environments
Design and implement advanced LLM Ops frameworks, ensuring deep observability, rigorous monitoring, logging, and evaluation specifically tailored for dynamic AI agents
Assist in refining and optimizing both modern Gen AI pipelines and existing classical ML models (feature engineering, hyperparameter tuning) for maximum accuracy, efficiency, and scalability
Collaborating with cross-functional teams to translate complex business needs into technical solutions

Who You Are (Success Profile)

You thrive in ambiguity and are comfortable building the path as you walk it, seeing dynamic environments as the raw material to build something meaningful
You act like an owner with a passion for the mission and a bias for action, navigating seamlessly between high-level strategy and hands-on execution
You are a problem-solver who seeks out challenges because you are energized by finding solutions that deliver the biggest impact
You are a learner with a growth mindset, actively seeking feedback to become a better partner and a stronger teammate
You are driven by innovation and have a deep curiosity for how things work, believing in the power of technology to accelerate transformation

What We’re Looking for (Minimum Qualifications)

3 to 5 years of related experience as an MLE, with a strong track record of building and deploying ML systems
Proven hands-on experience in creating Gen AI-based systems, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and a clear history of operationalizing Agentic AI in production
Deep understanding of MLOps and LLMOps principles, specifically dealing with the unique monitoring, observability, and debugg

Senior Machine Learning Engineer (Agentic AI)