Staff Machine Learning Engineer (Agentic AI/Gen AI)

Confirmed live in the last 24 hours

Zscaler

Bangalore, IND

Hybrid

Posted March 3, 2026

Job Description

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 seasoned Staff Machine Learning Engineer to join our Engineering team. This is a hybrid role based in Bangalore, reporting to the Manager of Machine Learning Engineering.

You will influence technical direction, bridge the gap between research and production, and drive technical excellence across the organization. You'll lead complex projects, mentor engineers, and build the scalable models and systems that power the world’s largest cloud security platform.

What you’ll do (Role Expectations)

Design and deploy scalable, reliable, and efficient production-grade Gen AI/ML systems from data ingestion to monitoring
Drive innovation by researching and evaluating emerging AI/ML frameworks, rapidly prototyping novel solutions, and championing full-scale implementation
Implement and maintain robust MLOps practices, including logging, monitoring, and CI/CD pipelines for distributed ML systems
Lead and mentor junior engineers in system design best practices while promoting a culture of technical excellence
Collaborate with cross-functional teams to translate complex business needs into high-impact technical solutions

Who You Are (Success Profile)

You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful
You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution
You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact
You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback—knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust
You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose

What We’re Looking for (Minimum Qualifications)

5+ years of experience as an MLE with a track record of shipping complex, scalable ML systems to production
Proven experience building Gen AI/ML systems with LLMs, including fine-tuning, Retrieval-Augmented Generation (RAG), and Agentic AI
Demonstrated expertise designing and implementing distributed ML systems with deep knowledge of ML

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