Testing Lead-QA

Telerad Group; Bengaluru; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Telerad Group in Bengaluru is seeking a hands‑on Testing Lead to own quality for Deep Learning, LLM, and Vision‑Language Model products. You’ll define how we test, measure, and document AI quality, collaborating with ML, engineering and product teams.

Strong documentation culture is essential; you’ll build clear strategies, maintain regression suites, and drive a quality-first approach in a fast‑moving startup. Remote/hybrid/on-site options are available to fit the team’s needs.

Qualifications

• 3–4 years hands-on ownership in DL/LLM/GenAI testing.
• Strong Python skills for test automation and data validation.
• Proven ability to write clear, structured technical documentation.

Responsibilities

• Define testing strategy for LLMs, VLMs, and DL pipelines.
• Create and maintain clear, lightweight documentation covering test strategies and acceptance criteria.
• Design tests for prompt templates and long-context conversations.
• Maintain golden datasets and regression test suites; report model behavior changes.
• Build Python-based automation and integrate tests into CI/CD pipelines.
• Produce readable quality reports and dashboards for engineers and leadership.

Skills

Software testing leadership

LLMs DL GenAI testing

Python for test automation

Technical documentation

Tools

LangChain

LlamaIndex

MLflow

Job description

Bangalore North, India | Posted on 07/14/2026

Job Title: Testing Lead – Deep Learning / LLM / VLM

Location

Remote /Hybrid / On-site

Experience

3–4 years (hands‑on ownership in DL / LLM / GenAI testing)

Employment Type

Full-time

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
  • 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.
• 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.