Company Description Klimber Technologies is an AI and cloud engineering firm recognized as an AI startup by the Government of India, with a granted Generative AI patent and over 20 years of Fortune 500 delivery experience. The company designs, builds, and operates production-grade data and AI platforms across AWS, Azure, GCP, Snowflake, and Databricks. Klimber focuses on migration, modernization, and end-to-end AI, ML, and LLM solutions for enterprise clients.

The team prioritizes scalable, cloud-native architectures and robust engineering practices. Klimber’s approach is to avoid vendor lock-in and to build capabilities that client teams fully own and can independently expand.

Role Description The AI Quality Engineer is a full time, remote role responsible for ensuring the reliability, safety, and performance of AI and data platforms delivered to clients. Day-to-day responsibilities include designing and executing test strategies for AI/ML models and data pipelines, validating model behavior against business requirements, and monitoring model performance and drift in production environments. The role involves developing automated test frameworks, quality gates, and metrics for cloud-based solutions across AWS, Azure, GCP, Snowflake, and Databricks.

The AI Quality Engineer collaborates closely with data scientists, ML engineers, and cloud architects to embed quality practices into the development lifecycle,



participates in code and model review processes, and contributes to continuous improvement of quality standards, documentation, and best practices.

Qualifications

- Strong Quality Engineering skills, including test strategy design, test automation, and quality metrics for AI and data platforms.
- Experience in Quality Assurance and Quality Control, with the ability to validate AI/ML models, data pipelines, and cloud-native services.
- Proven Product Quality focus, ensuring end-to-end reliability, performance, and usability of AI-driven solutions in production.
- Background in Quality Management, including process definition, continuous improvement, and alignment with industry standards.
- Hands-on experience with testing data pipelines and AI/ML workflows on one or more major cloud platforms (AWS, Azure, GCP) and tools such as Snowflake or Databricks.
- Familiarity with MLOps concepts, CI/CD pipelines, and monitoring tools for AI systems (e.g., model drift, bias, and performance monitoring).
- Proficiency in a scripting or programming language (such as Python) for building test harnesses and automation frameworks.
- Strong analytical and problem-solving skills, with attention to detail and the ability to work independently in a remote, distributed team.
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.

📌 AI Quality Engineer/LLM Evaluation Engineer (India)
🏢 Klimber Technologies
📍 India