Netskope: AI Agent Quality Engineer — Taguig, Taguig, Philippines

About Netskope

Netskope develops security and networking technology for cloud, data and AI environments. Its employer introduction identifies the Netskope One platform, a zero-trust engine and the NewEdge network as parts of that offering. The business describes giving organisations visibility and control while maintaining the performance of access to applications and information. This connects cybersecurity with networking: the products address both how digital resources are reached and how their use is controlled and protected.

The position and its setting

Netskope lists AI Agent Quality Engineer in Taguig, Taguig, Philippines, under reference 8078761. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Department: Information Technology
• Employer listing date: 22 July 2026

Subjects named in the job description

The posting mentions the following work areas. These references are not a separate list of compulsory certifications.

• Python
• CI/CD
• REST APIs
• APIs
• AI/ML
• LLMs
• AI
• audit
• compliance

Essential requirements and candidate background

• Build and grow the automated evaluation suite every agent runs against before it's approved for production, designed to run unattended and scale across a growing agent fleet, not something that needs a person babysitting each run
• As the fleet grows, build this into a repeatable, scriptable process rather than a manual exercise redone from scratch each time
• Partner with the Data Steward on data sensitivity classification for the systems agents touch, so your test scenarios reflect what's actually at stake, not a generic checklist
• Decide, for each agent capability, what "pass" actually means, and build that judgment into automated thresholds wherever possible so evaluation keeps up as the number of agents climbs into the hundreds
• Maintain the guardrail and negative-test catalog (fail-closed vs. fail-graceful behavior) across the platform, and add new cases as new failure modes get discovered in the wild
• Produce clear, audit-ready evidence for every agent's evaluation results, generated automatically as part of the pipeline rather than assembled by hand for each review
• Track drift over time across the whole fleet, not agent by agent, so a slow-moving problem in one corner doesn't go unnoticed just because no one's looking at that specific agent that week
• Must-Have:
• At least 4 years in software quality, security testing, or a related discipline, with 1–2 years specifically evaluating or red-teaming LLM-based systems — not just running unit tests against traditional code
• Python skills, since the evaluation harness, adversarial test scripts, and automated pipelines will mostly be built in it
• Comfortable writing production-quality code, not just glue scripts
• Knowledge: REST APIs and webhook/event-driven patterns, enough to build test harnesses that call an agent's tools directly and validate its inputs and outputs, not just its final chat response
• Real experience building automated test infrastructure and integrating it into CI/CD, not just manually running test cases — someone who thinks in pipelines and repeatability by default
• Practical understanding of prompt injection, jailbreaking, and sycophancy failure modes — able to design new test cases for these, not just run ones someone else wrote
• Comfort making a hard call: willing to block a release when an agent doesn't meet its bar, even under schedule pressure
• Advantage:
• Enough understanding of data sensitivity and compliance classification to design tests that reflect real risk, even though the Data Steward owns the classification system itself
• Experience: a regulated environment where evaluation results had to hold up to an external audit, not just an internal review
• Familiarity: multi-attempt or persistent-attack testing methodology, rather than only single-shot adversarial prompts
• Some exposure to how agents are actually built (prompting, tool schemas, orchestration) — not required, but it makes it much easier to design tests that target real failure modes instead of generic ones

Location, schedule and contract conditions

For this overseas or regional listing, confirm work authorisation and any location restrictions with the employer. The location does not establish visa sponsorship or permission to work remotely from India.

• Advertised location: Taguig, Taguig, Philippines

Conditions requiring the complete notice

Some passages contain complex or combined conditions that have not been reduced to short eligibility statements here. Read the complete linked notice for those passages, including any alternatives, exceptions or restrictions. The concise facts above should not be treated as the full wording of every condition.

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another AI Agent Quality Engineer vacancy is not an offer for reference 8078761; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to Netskope

Applications follow the official employer page linked below.

Open the official Netskope posting below and match the title and reference 8078761.

Compare your qualifications and experience with the essential conditions for the Taguig, Taguig, Philippines position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: Python

Python work is easier to assess through a concrete example than a list of libraries. Explain the problem, the input data, the parts you implemented and how you checked the output. Include a repository or a short technical note when you can share it.

Relevant skills

• Python
• CI/CD
• REST APIs
• APIs
• AI/ML
• LLMs
• AI
• audit
• compliance