TL;DR

Machine Learning Engineer (GenAI): Building and scaling GenAI platforms and LLM-powered services with an accent on cloud infrastructure integration and production deployment. Focus on designing scalable AI pipelines, collaborating cross-functionally, and optimizing GenAI components for real-world impact.

Location: Hybrid in Rotterdam with up to 40% office, 40% home, and 20% flexible work; manager approval required for up to 3 weeks abroad per year

Salary: €80,000–€110,000 gross annually including FlexBudget, holiday allowance, and potential bonus or profit sharing

Company

%hirify_global% is committed to climate neutrality by 2035, offering innovative digital solutions to help customers become greener through modernized digital platforms.

What you will do

Build and maintain scalable GenAI components and pipelines for LLM-powered services.
Design and implement RAG pipelines combining LLMs with vector search technologies.
Collaborate with platform owners, data scientists, and engineers to develop AI-powered features.
Develop user interfaces or backend systems interacting efficiently and safely with LLMs.
Monitor platform usage, costs, and GenAI/LLM-related metrics.
Create evaluation frameworks for chatbot and GenAI applications.

Requirements

Must have experience with Terraform or similar Infrastructure as Code tools.
Experience working in platform teams and designing multi-use systems.
Hands-on experience with LLM APIs (OpenAI, Anthropic, Azure OpenAI) and open-source models.
Strong programming skills in Python and ML/AI toolkits like PyTorch, Hugging Face Transformers, LangChain.
Experience deploying ML/AI systems in production environments using Docker, Kubernetes, or MLflow.
Understanding of software engineering principles including testing, CI/CD, version control, and containerization.

Nice to have

Experience building AI-enabled applications such as chatbots or intelligent agents.
Experience with REST APIs, microservices, and scalable backend systems.
Familiarity with prompt engineering, RAG, and model fine-tuning techniques.
Production experience with Azure cloud services.
Flexibility with pro-code, low-code, or no-code solutions.
Background in Machine Learning, NLP, or AI Engineering.

Culture & Benefits

Flexible hybrid working model with office, home, and limited abroad work options.
Personal and professional growth support.
FlexBudget allowing payout, extra holidays, or savings.
Commitment to climate-neutral mission by 2035.
Focus on work-life balance and flexible schedules.

Hiring process

Contact recruiter for more information and application support.