Software Engineer

KPN Corporation

Skills

Design and build AI/ML models for HR use cases.
Examples include attrition prediction, talent and skill matching, workforce planning, performance analytics, and employee sentiment analysis.
Develop LLM-based solutions.
Build systems such as Retrieval-Augmented Generation (RAG) over HR policies and knowledge bases, and use prompt engineering and fine-tuning where appropriate.
Build and maintain data pipelines.
Extract, clean, and transform data from the HRIS, payroll, attendance, recruitment, and learning systems so it is ready for AI models.
Deploy and monitor models in production (MLOps).
Manage versioning, performance monitoring, drift detection, and retraining so models stay accurate over time.
Define the HRIS AI architecture.
Select appropriate tools, cloud services, and AI platforms in line with company IT standards.
Ensure responsible AI and data protection.
Test models for bias and fairness, especially in hiring and promotion, and comply with data privacy regulations [e.g., local Personal Data Protection Law / GDPR].
Translate HR problems into AI solutions.
Work with HR business partners and HRIS teams to gather requirements, define success metrics, and measure business impact.
Evaluate emerging AI technologies.
Assess new tools and vendors and recommend those that fit the HR technology roadmap.
Document and share knowledge.
Write technical documentation and train HR and HRIS team members to understand and use AI tools.

Bachelor's degree in Computer Science, Informatics, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related field.
Minimum 3–5 years of experience as an AI Engineer, Machine Learning Engineer, or similar role, with a proven record of deploying models into production.
Strong programming in Python, including libraries such as Pandas, NumPy, and Scikit-learn.
Hands-on experience with ML/deep learning frameworks such as TensorFlow or PyTorch.
Practical experience with LLMs and GenAI, such as the OpenAI, Anthropic Claude, or Azure OpenAI APIs, LangChain or LlamaIndex, and vector databases.
Solid SQL skills and experience with data warehouses or data lakes.
Experience with a major cloud platform (AWS, Azure, or GCP) and its AI/ML services.
Familiarity with MLOps tools and practices, such as Docker, CI/CD, MLflow, and model monitoring.
Understanding of data privacy, security, and AI ethics, particularly for sensitive personal data.

A Master's degree in AI, Machine Learning, or Data Science is an advantage.
Experience with HR data, HRIS platforms, or people analytics is a strong advantage.