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Lead Software Development Engineer in Test

  • Source: LinkedIn
  • Neo
  • On-site
  • Bengaluru
  • Full-Time
  • Senior

Job Description

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.
  • Drive — AI-native repository, where humans and agents can co-work on files.
  • Scribe — AI assistant for meetings that goes beyond note-taking and actually does your work while on the call.

About the role

As a Lead SDET you will set the technical direction for test strategy and quality across Neo’s engineering organization, and mentor the SDETs and QA engineers who work within it.

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 own and oversee this approach, from design through to production.

Your scope isn’t one team’s test suite — it’s the frameworks, standards, and quality gates every squad builds on, and the org-wide roadmap for how Neo uses AI in testing.

Job Location

Bangalore, India (100% on-site)

Responsibilities

Architecture & technical direction

  • Architect centralized test automation frameworks for UI and backend (REST/GraphQL) that multiple squads adopt, rather than each team building its own.
  • Serve as the deepest technical expert on software internals for testing — unblock teams on complex distributed-system problems and give architectural guidance where a single squad can’t resolve it alone.

AI quality strategy & harness

  • Define the roadmap for adopting AI tools and LLMs in testing, and lead evaluation of new AI coding assistants and smart-automation capabilities (self-healing locators, AI-driven visual testing) before rolling them out org-wide.
  • Standardize how the organization validates AI-powered features — model accuracy checks, hallucination and prompt-injection testing — and build the automated pipelines for prompt evaluation and data-quality verification that make this systematic rather than ad hoc.

CI/CD & release strategy

  • Own the vision for testing infrastructure: architect parallelized CI/CD pipelines, optimize test execution time and cloud cost, and act as the gatekeeper for production releases.
  • Design test environments and grids on Kubernetes/Docker that scale with the org’s testing load.

Mentoring & raising the bar

  • Lead and mentor a team of SDETs and QA engineers; set the coding standards and architecture-review process the whole org tests against.
  • Build the quality culture — shift-left practices, code review discipline — and turn what works on one team into the standard for all of them.

Organizational quality & product partnership

  • Partner with engineering managers, directors, and product leadership so testability and quality are designed into the architecture from day one, not bolted on after.
  • Define and report department-level quality KPIs (DORA metrics, escaped-defect rate, coverage trends) to leadership, and spearhead cross-functional initiatives where quality bottlenecks span dev, QA, and DevOps.

Skills

  • AI-native engineering practice — Evaluates, selects, and rolls out AI coding assistants and AI-augmented testing capabilities at an organizational level; builds the automated pipelines that validate AI/LLM-powered features (prompt evaluation, data-quality verification) systematically.
  • Programming & standards — Expert in Java and JavaScript/TypeScript; defines coding standards, leads architecture reviews, and keeps enterprise test repositories scalable and maintainable.
  • Systems architecture — Deep, system-level understanding of microservices, RESTful and GraphQL APIs, HTTP/HTTPS, and asynchronous messaging systems.
  • **Test…

Skills

  • zeta
  • the-org
  • GraphQL
  • Kubernetes
  • Docker
  • Java
  • javascript
  • TypeScript
  • microsoft-typescript
  • Playwright
  • Cypress
  • selenium
  • postman
  • testng
  • Prometheus
  • Grafana
  • splunk

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