Senior Machine Learning Engineer (Agentic AI)

Zscaler · Bengaluru · 3–5 yrs experience · Posted 2026-07-18

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

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About the role

Responsibilities:
- 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.
- 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
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
- 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

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
- 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

Responsibilities

- 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.
- 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

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