Testing Lead

Role Overview

We are seeking a hands-on Testing Lead to own quality and documentation for our Deep Learning, LLM, and Vision-Language Model (VLM) products. You will define how we test, measure, document, and communicate AI quality—working closely with ML, Engineering, and Product teams in a fast-paced startup environment.

This role is ideal for someone who believes clear documentation is as critical as good testing, especially for non-deterministic AI systems.

What You’ll Do

Own Quality & Documentation End-to-End

• Define testing strategy for LLMs, VLMs, and DL pipelines.
  
• Create and maintain clear, lightweight documentation covering:
  
• Model testing strategies and assumptions
  
• Evaluation metrics and acceptance criteria
  
• Known limitations, risks, and failure modes
  
• Release readiness and quality sign-off
  
• Ensure documentation evolves with models, data, and prompts.
  

LLM / GenAI Testing

• Design tests for:
  
• Prompt templates and prompt changes
  
• RAG pipelines (retrieval quality, grounding, hallucination control)
  
• Multi-turn conversations and long-context behaviour
  
• Maintain golden datasets, regression test suites, and test result summaries.
  
• Document prompt behaviour, edge cases, and known model quirks.
  

Vision & Multimodal Testing

• Test VLMs for image-text alignment, OCR, captioning, and reasoning.
  
• Document model performance across different image types, quality levels, and domains.
  
• Track and publish model behaviour changes between versions.
  

Automation, MLOps & Reporting

• Build Python-based automation for evaluation and regression testing.
  
• Integrate tests into CI/CD and MLOps pipelines.
  
• Produce readable quality reports and dashboards for engineers and leadership.
  
• Monitor and document production issues such as model/data drift and degradation.
  

Build a Quality-First Culture

• Establish QA and documentation standards that scale with a startup.
  
• Mentor engineers on writing testable code and meaningful documentation.
  
• Act as the single source of truth for AI quality, testing, and known risks.
  

What we’re looking For

Must-Have

• Strong background in software testing with lead or ownership experience.
  
• Hands-on experience testing LLMs, DL models, or GenAI systems.
  
• Strong Python skills for test automation and data validation.
  
• Proven ability to write clear, structured technical documentation.
  
• Understanding of:
  
• Transformer-based models and DL workflows
  
• Model evaluation metrics and non-deterministic system testing
  
• Comfortable working in ambiguity and moving fast in a startup.
  

Nice-to-Have

• Experience with VLMs, multimodal models, or computer vision.
  
• Exposure to RAG architectures, vector databases, and embeddings.
  
• Familiarity with tools like LangChain, LlamaIndex, MLflow, or similar.
  
• Experience documenting AI risks, limitations, or compliance requirements.
  

 Interested can apply to