Staff Machine Learning Engineer (Agentic AI)

Zscaler · Bengaluru · 5+ yrs experience · Posted 2026-07-18

Tech stack: AWS, Azure, GCP, Go, Python, Rust, SQL

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About the role

Responsibilities:
- 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.
- 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
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
- 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

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
- 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

Responsibilities

- 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.
- 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

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