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 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 debugging challenges of non-deterministic AI agents
• A solid computer science foundation (data structures, algorithms) coupled with expertise in Python (scikit-learn, PyTorch/TensorFlow) and SQL for comprehensive feature engineering and model evaluation
• Excellent communication and interpersonal skills, with the ability to work independently and as a strong team player

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
• Familiarity with systems programming or distributed systems
• A demonstrated interest in innovation, such as participation in tech blogs, external papers, or industry knowledge-sharing