QA Azure AI Engineer EDV WERKE AG Building on experience, results and commitment.

Edvwerke; Kuala Lumpur; On-site; MYR 100,000 - 130,000; Full time

Benefits offered by this job

Competitive salary with performance-based bonuses

Opportunities for professional development

Dynamic and collaborative work environment

Job summary

Edvwerke is seeking a QA Azure AI Engineer to develop and implement AI solutions in Kuala Lumpur. Key responsibilities involve building generative AI models and maintaining AI deployments on cloud platforms.

The ideal candidate has extensive experience in Python, AI evaluation, and deploying models in cloud services. The position offers a competitive salary with performance-based bonuses and a dynamic work environment.

Qualifications

• 1–2 years hands‑on experience with generative AI and RAG architecture.
• 5+ years deploying AI models on cloud AI services.
• 7+ years coding in Python, including complex scripting.
• 8+ years QA experience with LLM evaluation and test automation.
• 5+ years building pipelines for automated AI model testing and deployment.
• Practical experience deploying and managing AI models in cloud AI services.

Responsibilities

• Build and implement AI solutions using generative models and RAG pipelines.
• Deploy, manage, and maintain AI models on cloud platforms.
• Write complex Python scripts for data processing.
• Evaluate models using advanced metrics.
• Develop automated pipelines for AI testing.
• Provide guidance on AI model architecture.
• Collaborate with cross-functional teams.
• Maintain reproducible workflows and documentation.

Skills

Generative AI Expertise

Cloud Platform Experience

Python Specialist

QA & Automation Knowledge

DataOps & CI/CD

Technical Degree

Azure AI Deployment

Evaluation Metrics Mastery

Education

Degree in CS, Engineering, Math

Tools

Python (Pandas, PyTest)

AI Deployment Tools

Job description

EDV Werke is looking for a QA Azure AI Engineer

Working Model: On-site, Kuala Lumpur

Form of cooperation: B2B Contract

Tasks

• Generative AI Development – Build and implement AI solutions using generative models and RAG pipelines.
• Cloud AI Deployment – Deploy, manage, and maintain AI models on cloud platforms with scalable architecture.
• Python Engineering – Write complex Python scripts for data processing, testing, and model automation.
• QA & Evaluation – Evaluate models using advanced metrics, including LLM-as-a-Judge and deterministic assertions.
• DataOps & CI/CD Pipelines – Develop automated pipelines for AI testing and deployment on cloud platforms.
• Technical Leadership – Provide guidance on AI model architecture and best practices for deployment.
• Cross-team Collaboration – Work with data, backend, and product teams to integrate AI solutions into production.
• Documentation & Reporting – Maintain reproducible workflows, technical documentation, and experiment results.

Must-Have Skills

• Generative AI Expertise – 1–2 years hands‑on experience with generative AI and RAG architecture (vector search + LLM).
• Cloud Platform Experience – 5+ years deploying AI models on cloud AI services, not just locally.
• Python Specialist – 7+ years coding in Python, including complex scripting (Pandas, PyTest) beyond basic automation.
• QA & Automation Knowledge – 8+ years QA experience with LLM evaluation and test automation.
• DataOps & CI/CD – 5+ years building pipelines for automated AI model testing and deployment.
• Technical Degree – Degree in CS, Engineering, Math, or equivalent heavy experience.
• Azure AI Deployment – Practical experience deploying and managing AI models in cloud AI services.
• Evaluation Metrics Mastery – Ability to design and interpret advanced AI evaluation metrics and regression testing.

Should-Have Skills

• Agentic AI / Multi-Agent Systems – Experience with agentic AI frameworks or multi-agent AI systems.
• Cloud Search Expertise – Experience with cloud AI search services, including indexing, vector profiles, and hybrid search.
• Python AI Frameworks – Experience using LangChain or Semantic Kernel for AI test harnesses.
• Evaluation Pipelines – Experience using prompt flow to build model evaluation pipelines.
• Golden Dataset Creation – Knowledge of creating ground truth datasets for regression testing.
• Master’s Degree – Advanced degree in Data Science or AI‑related field.

Nice to Have

• Small Model Fine‑Tuning – Experience fine‑tuning small language models like Phi‑3 or LLaMA.
• Cloud ML Tools – Experience managing ML workspaces, compute instances, and experiment tracking.
• Backend Integration Skills – Background in C# / .NET for AI integration with backend systems.
• Security Testing – Experience with red‑teaming, adversarial, or jailbreak testing for AI models.

Benefits

• Competitive salary with performance‑based bonuses.
• Opportunities for professional development and advancement.
• Dynamic and collaborative work environment.