AI Validation engineer

1 tydzień temu

Gdańsk, Pomerania, Polska KBC Technologies Group Pełny etat 55 000 zł - 85 000 zł rocznie

Job description: AI Validation engineer

Validation Engineer to ensure the quality, reliability, and compliance of our dual-pipeline data ingestion systems and full RAG application deployments. This critical role involves designing, executing, and documenting validation protocols to verify that all data processes and AI applications meet established specifications and regulatory standards

Key Responsibilities

Develop Validation Strategies: Design comprehensive validation master plans and test strategies for the entire RAG system lifecycle, from data ingestion to model deployment.

Create and execute and Performance Qualification for data pipelines, vector databases (e.g., Pinecone), and RAG application components.

Validate the dual data pipelines (batch and streaming) to ensure data is accurately collected, transformed, and loaded, including data quality checks, schema validation, and error handling mechanisms.

Conduct functional and non-functional testing of the RAG application, focusing on the retrieval workflow, prompt engineering, embedding strategies, and overall response accuracy and relevance.

Documentation & Compliance: Maintain detailed documentation of all validation activities, test results, deviations, and corrective actions, ensuring compliance with relevant industry standards and internal quality management systems.

Cross-functional Collaboration: Work closely with data scientists, ML engineers, software developers, and product managers to define requirements, troubleshoot issues, and implement continuous improvements.

Qualifications

A Bachelor's degree in a technical field such as Engineering, Computer Science, or Data Science is required.

Polish speakers capable of working with Polish LLM

Candidates should have experience as a Validation or QA Engineer, preferably with a focus on data quality and testing in a regulated or high-tech setting.

Key technical skills include proficiency in Python and SQL for data tasks, experience with data orchestration tools, familiarity with cloud platforms, experience with vector databases and embedding techniques for RAG systems, and knowledge of SDLC and QA methodologies.

Essential soft skills include strong analytical and problem-solving abilities, excellent communication skills, and the ability to collaborate in a team environment.

Prefer experience with AI/ML testing frameworks and automation.

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