2 months ago

Senior Machine Learning Engineer (Agentic AI)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Senior ML Engineer in the Exposure Management & Security Operations team driving end-to-end GenAI agentic AI developments, including productionizing AI agents, building advanced LLMOps frameworks, and collaborating with cross-functional teams to translate business needs into scalable AI solutions for a large cloud security platform.

Location: Bengaluru

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Manager level

Key Responsibilities

•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.

Pay and Benefits

Perks:Health InsurancePar Poperal

Key Requirements

•3 to 5 years of related experience as an MLE, with a strong track record of building and deploying ML systems.
•Hands-on experience in creating Gen AI-based systems, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and operationalizing Agentic AI in production.
•Deep understanding of MLOps and LLMOps principles, specifically dealing with monitoring, observability, and debugging of non-deterministic AI agents.
•Solid computer science foundation (data structures, algorithms) with expertise in Python (scikit-learn, PyTorch/TensorFlow) and SQL for feature engineering and model evaluation.
•Excellent communication and interpersonal skills, with the ability to work independently and as a strong team player.

Experience:3-5 yearsCybersecurityAICloud

Skills:CommunicationProblem-solvingTeamworkAdaptabilityOwnership

Languages:English

Tech Stack:PythonScikit-learnPyTorchTensorFlowSQLAWSGCPAzureKubeflowSageMakerLLM

Company Brief

Zscaler

Provides cloud-native security platform delivering secure access, threat protection, and zero trust services to organizations, enabling secure internet and private application access without traditional network appliances.

Industry: Cybersecurity

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: San Jose, United States

Founded: 2007

Website LinkedIn

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Zscaler

2 months ago

Senior Machine Learning Engineer (Agentic AI)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Location: Bengaluru

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Manager level

Key Responsibilities

•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.

Pay and Benefits

Perks:Health InsurancePar Poperal

Key Requirements

•3 to 5 years of related experience as an MLE, with a strong track record of building and deploying ML systems.
•Hands-on experience in creating Gen AI-based systems, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and operationalizing Agentic AI in production.
•Deep understanding of MLOps and LLMOps principles, specifically dealing with monitoring, observability, and debugging of non-deterministic AI agents.
•Solid computer science foundation (data structures, algorithms) with expertise in Python (scikit-learn, PyTorch/TensorFlow) and SQL for feature engineering and model evaluation.
•Excellent communication and interpersonal skills, with the ability to work independently and as a strong team player.

Experience:3-5 yearsCybersecurityAICloud

Skills:CommunicationProblem-solvingTeamworkAdaptabilityOwnership

Languages:English

Tech Stack:PythonScikit-learnPyTorchTensorFlowSQLAWSGCPAzureKubeflowSageMakerLLM

Company Brief

Zscaler

Industry: Cybersecurity

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: San Jose, United States

Founded: 2007

Website LinkedIn