Senior Software Development Engineer in Test

About Neo

AI is the most significant shift in how work gets done since the advent of the Internet — yet most organizations fail to capture its value. Neo is changing that.

Founded by Bhavin Turakhia — Co-Founder of Zeta , Radix , and Titan — Neo is building an integrated, AI-first suite of products that capture work, centralize context, and make AI a first-class participant in every workflow:

• Tasket — A radically reimagined, AI-native, work management platform that centralizes context and makes AI delegation seamless for all work.
• Friday — AI Assistant, co-worker and agent platform, pre-integrated into Tasket, Studio, Drive and 1000+ SaaS platforms.
• Studio — AI-native suite for co-creating docs, spreadsheets and diagrams collaboratively with humans and agents.
• Scribe — AI assistant for meetings that goes beyond note-taking and actually does your work while on the call.

About the role

As a Senior SDET you will own the test architecture and quality bar for the systems your team ships, and mentor other engineers on testability and clean automation practice.

We are proponents of leveraging best-in-breed AI tools at every step of the engineering lifecycle — design, development, review, and testing. This requires a different engineering mindset: a lean team of senior, hands-on, AI-adept engineers, resulting in ultra-rapid iteration cycles and fast output. You will help drive this approach, from design through to production.

Beyond the systems you personally test, you’ll set the standards other engineers test against — the frameworks, CI/CD scaffolding, and quality gates that let the rest of the team move fast without breaking things.

Job Location

Bangalore, India (100% on-site)

Responsibilities

AI-powered QA

• Design and execute test strategies for AI-powered features — checking model accuracy, catching hallucinations, and probing for prompt-injection vulnerabilities.
• Evaluate AI/LLM integrations directly: prompt evaluation, data pipeline verification, and the failure modes generic functional tests miss.
• Use AI-augmented testing techniques — self-healing locators, AI-driven visual testing — to keep automation resilient as the UI changes.

Agent leverage & AI-first workflow

• Use AI coding assistants to accelerate script creation, framework development, and root-cause analysis when a test fails.
• Treat agents as part of the testing lifecycle: structure the work so they can execute reliably, and stay the last line of quality on what they produce.

Framework & architecture

• Design and build test automation frameworks from the ground up for UI and backend (REST/GraphQL) surfaces — reusable, maintainable, and shared across teams rather than rebuilt per feature.
• Make the calls on framework structure, tooling, and where the complexity should live.

CI/CD, performance & reliability

• Architect scalable, parallelized CI/CD pipelines that cut test execution time and infrastructure cost.
• Define and run load, stress, and performance testing for high-throughput microservices.
• Use observability tooling (Prometheus, Grafana, Splunk) to diagnose test failures and correlate them with real system health.

Mentoring & quality culture

• Mentor QA and development engineers on testability, clean test code, and shift-left practices.
• Drive proofs-of-concept for new tools and lead cross-functional quality initiatives.
• Track and report quality metrics, catching regressions in trend before they hit production.

Skills

• AI-native engineering practice — Fluent with AI coding assistants for test generation and debugging, and with AI-specific testing methodology: prompt evaluation, hallucination and prompt-injection checks, data pipeline verification, self-healing locators, and AI-driven visual testing.
• Programming & automation — Strong, practical skills in Java and JavaScript/TypeScript; deep experience with Playwright, Cypress, or Selenium for UI testing and Postman, TestNG, or RestAssured for API testing.
• Systems & architecture — Solid understanding of microservices, RESTful and GraphQL APIs, and HTTP/HTTPS, with proven experience automating tests for large-scale, distributed SOA systems.
• Infrastructure & observability — Hands-on with Kubernetes and Docker, deploying test infrastructure in cloud environments, and using Prometheus, Grafana, or Splunk to read system health and failure signals.
• **Perfor…