Senior Machine Learning Engineer (Agentic AI)

About Zscaler

Zscaler accelerates digital transformation to ensure our customers can be more agile, efficient, resilient, and secure. As an AI-forward enterprise, we are constantly pushing the envelope, leveraging the world’s largest security data lake to power our cloud-native Zero Trust Exchange platform. This innovation protects our customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location.

Here, impact in your role matters more than title and trust is built on results. We say, impact over activity. We seek innovators who actively use AI to amplify their impact and who thrive in an environment where we leverage intelligent systems to stay ahead of evolving threats. We believe in transparency and value constructive, honest debate—we’re focused on getting to the best ideas, faster. We build high-performing teams that can make an impact quickly and with high quality. To do this, we are building a culture of execution centered on customer obsession, collaboration, ownership, and accountability.

We value high-impact, high-accountability with a sense of urgency where you’re enabled to do your best work and embrace your potential. If you’re driven by purpose, thrive on solving complex challenges, and want to be part of the team that’s helping to secure the AI age, we invite you to bring your talents to Zscaler and help shape the future of cybersecurity.

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 LLMOps frameworks, ensuring deep observability, rigorous monitoring, logging, and evaluation specifically tailored for dynamic AI agents

Assist in refining and optimizing both modern GenAI 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