Role Overview
Duration: (1- year Contract)

We are looking for a smart, detail‑oriented QA Engineer who is passionate about AI quality, data validation, and model evaluation. In this role, you will assess the correctness of AI model outputs, analyse datasets, identify trends, and help ensure that customer‑focused MVPs meet real‑world expectations.
This position is ideal for someone strong in analytical thinking, data intuition, and product understanding—even if not deeply technical in coding.

Key Responsibilities

AI Model Testing & Evaluation

• Validate predictions produced by AI/ML models (CV, NLP, LLMs, chatbots).
• Design and execute test cases on internal testing platforms using “hard datasets”, edge cases, and adversarial inputs.
• Functional and performance testing of AI Products (Beta version) on internal and customer datasets
• Evaluate AI workflows using prompt engineering to test instructions, responses, and failure modes.
• Provide structured, actionable feedback to engineering and research teams.

Data Quality & Analytics

• Assess data quality, identify gaps, and flag anomalies in training and evaluation datasets.
• Explore, curate, and validate datasets for customer-centric MVP development.
• Perform trend analysis, descriptive analytics, and insights reporting for customer and internal teams.

Data Workflow & Annotation

• Use data annotation tools to label or validate images, text, or multimedia datasets.
• Work with internal data pipelines for ingestion, preprocessing, and quality checks.
• Conduct web scraping or data collection for targeted tasks (basic Python/automation scripts is a plus).

Product Feedback & QA Processes

• Act as the “first user” of AI prototypes and MVPs; provide usability and product behaviour feedback.
• Maintain QA documentation, test reports, and evaluation scorecards.
• Collaborate with engineers, product managers, and researchers to improve model performance and robustness.

Required Skills & Experience

• 2–5 years of experience in QA, data evaluation, data annotation, or AI testing roles.
• Accelerated or high velocity annotation through creative tools
• Strong understanding of data workflows and basic ML concepts (classification, detection, NLP).
• Familiarity with data annotation platforms (Label Studio, CVAT, Super Annotate, etc.).
• Strong analytical, communication, and documentation skills.
• Experience with web scraping, dataset exploration, and data validation.

Preferred Skills (Nice to Have)

• Ability to design and execute structured test plans for AI/ML products.
• Exposure to prompt engineering (LLM testing, red-teaming, persona-based prompts).
• Basic coding knowledge (Python preferred) for data checks and scripting.
• Understanding of evaluation metrics for CV/NLP/LLM systems.
• Familiarity with modern AI tools like HuggingFace, OpenAI APIs, or vector search systems.
• Experience in testing chatbots, multimodal models, or enterprise AI tools.

Who Will Succeed in This Role?

Someone who is:

• Curious and detail‑oriented
• Strong in pattern recognition and data intuition
• Comfortable exploring new datasets and edge cases
• Able to think like both a tester and a user
• Passionate about AI quality and safety