Senior QA Automation Engineer

About Us

Evolphin is building the next generation of AI-powered media workflows for enterprise media teams managing large image and video libraries at scale, including environments with tens of millions of video assets and extremely large metadata and embedding footprints. Its platform adds a conversational AI layer for extracting intelligence from media, enabling powerful search, conversational discovery, and automation of media workflows through AskAI.

We're now pushing further into AI — building the next generation of intelligent, automation-first media management — and we're looking for people who want to help build that future from the ground up.

Role Overview

We are looking for a QA Automation Engineer (5–8 years experience) who can take ownership of automation strategy and quality engineering for our AI-powered platform.

This role is primarily automation-focused, but will also require hands-on manual testing for new features, exploratory scenarios, AI output validation, and edge-case workflows. You will play a critical role in ensuring the reliability, usability, performance, and scalability of our image and video automation workflows.

This role is ideal for someone who thrives in fast-moving startup environments and enjoys building automation frameworks while staying close to product behavior through manual validation when needed.

Key Responsibilities

•

• Design and own the end-to-end automation framework for web, API, and AI-driven features

• Build API automation suites for our core automation workflows and integrations (Postman/Newman/RestAssured)

• Drive UI automation using Selenium, Playwright, or Cypress

• Ensure cross-browser compatibility for our design-heavy, canvas-based interfaces

• Build performance testing suites to validate image/video processing throughput under load

• Embed quality gates into our deployment pipelines (GitHub Actions/Jenkins)

• Enable rapid, confident releases through automated testing at every stage

• Create validation frameworks for non-deterministic ML results—balancing precision with creative flexibility

• Partner with Product to define acceptance criteria for AI-driven features

• Work alongside Engineering to shift-left quality and build testability into system design

• Experience using LLMs (ChatGPT, GitHub Copilot, Cursor, etc.) to generate and refactor automation scripts

• Ability to review, validate, and harden AI-generated test code (not blindly accept output)

• Experience building prompt templates for test case generation from user stories

• Familiarity with AI-assisted test case generation and coverage analysis

• Understanding of how to test AI-driven features (prompt testing, hallucination detection, cost optimizations, response validation)

• Ability to design deterministic validation strategies for probabilistic AI outputs

• Experience integrating automation into AI-heavy workflows (semantic search, generative features, etc.)

Requirements

• 5 to 7 years of hands-on QA automation experience in fast-paced product environments

• Web Automation: Deep expertise in Playwright, or Cypress—you know when to use which

• API Testing: Proven experience with Postman, Newman, RestAssured, or similar tools

• AI/ML Testing: Experience validating machine learning systems or computer vision pipelines

• Programming: Proficiency in Java, Python, or JavaScript—you can read and write production code

• CI/CD: Experience integrating tests into GitHub Actions, Jenkins, or similar pipelines

• SaaS Domain: Track record testing cloud-based, multi-tenant products at scale

• Startup DNA: Comfortable with ambiguity, rapid iteration, and wearing multiple hats

Good to Have

• Visual Domain: Background in image/video processing, creative tools, or digital asset management

• Performance Engineering: Hands-on with JMeter, k6, or Gatling for load testing

• Cloud Platforms: AWS or GCP experience, particularly with serverless or containerized architectures

Benefits

• Impact at scale: Build the backbone of a platform trusted by major brands and enterprises to automate critical creative workflows.

• Innovation-driven: Collaborate at the intersection of backend, AI, and cloud automation.

• Growth opportunities: Lead backend strategy while mentoring and shaping engineering practices.

• Flexible work environment: Support for remote arrangements tailored to your needs.

• AI-First Product: You're not testing CRUD apps—you're validating cutting-edge computer vision and generative AI workflows that didn't exist three years ago.

Originally posted on Himalayas