Staff 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 Staff Machine Learning Engineer to join our Exposure Management & Security Operations team. This role is a hybrid position based in Bangalore, reporting to the Sr. Manager, Engineering Strategy, Planning & Analytics. 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)

• Designing and deploying scalable, reliable, and efficient production-grade Gen AI/ML systems from data ingestion to monitoring
• Driving innovation by researching and evaluating emerging AI/ML frameworks, rapidly prototyping novel solutions, and championing full-scale implementation
• Implementing and maintaining robust MLOps practices, including logging, monitoring, and CI/CD pipelines for distributed ML systems
• Leading and mentoring junior engineers in system design best practices and promoting technical excellence
• 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)

• At least 5 years of experience as a Machine Learning Engineer with a track record of shipping complex, scalable ML systems to production
• Proven experience building Gen AI/ML systems with LLMs, fine-tuning, Retrieval-Augmented Generation (RAG), and Agentic AI in production environments
• Experience designing and implementing distributed ML systems with deep knowledge of MLOps, including monitoring and logging
• Strong computer science foundation in data structures, algorithms, and system design with expertise in Python and SQL
• Excellent communication and interpersonal skills to partner effectively across global engineering teams

What Will Make You Stand Out (Preferred Qualifications)

• Expertise with cloud services such as AWS, GCP, or Azure and ML platforms like Kubeflow or SageMaker
• Proficiency in systems programming languages like Go or Rust with an understanding of distributed systems and networking fundamentals
• A record of research, publications, or patents in AI/ML