AI/ML Engineer – Client‑Facing AI Solutions

About the role

Craftzing and Sagacify are seeking an AI/ML Engineer who can move seamlessly between client workshops and code development. You will translate fuzzy business challenges into production‑grade AI solutions, ranging from LLM‑powered assistants to predictive models, and clearly explain why they work.

Key responsibilities

• Understand client business needs and design end‑to‑end AI solutions that create real value.
• Run discovery sessions and technical workshops with both business and technical stakeholders.
• Build AI pipelines that combine LLMs, vector databases, APIs, orchestration layers and user interfaces.
• Develop agents and Retrieval‑Augmented Generation (RAG) pipelines to connect client data with LLMs.
• Perform prompt and context engineering to improve output quality and reduce hallucinations.
• Build, train and evaluate classical ML models (classification, regression, forecasting, clustering) when appropriate.
• Handle data gathering, cleaning, validation and feature engineering.
• Evaluate solutions with ML and generative‑AI metrics, monitor production drift and costs.
• Deploy cloud‑based solutions with attention to security, reliability, versioning and reproducibility.
• Present AI concepts, trade‑offs and results to non‑technical audiences and support sales in pre‑sales activities.

Required profile

• Master’s degree in Computer Science, Engineering, Data Science, Mathematics, Statistics or a related field.
• 2–5 years of experience in AI/ML engineering or a closely related role.
• Proficiency in Python and solid software development best practices.
• Strong fundamentals in supervised and unsupervised learning, feature engineering and model validation.
• Excellent communication skills in English and French or Dutch, with a consultative mindset.

Required skills

• Python
• Pandas, NumPy, Scikit‑learn
• PyTorch or TensorFlow
• OpenAI API, Hugging Face, LangChain / LangGraph
• FastAPI, Docker
• SQL databases
• LLM fundamentals: Transformers, generation parameters, RAG, fine‑tuning
• Experience with major cloud providers (AWS, Azure or GCP)
• MLOps tools: CI/CD, MLflow, DVC, monitoring, Kubernetes

What we offer

• Human‑sized environment with strong team spirit.
• Flexible working arrangements.
• Trust and space to take ownership of projects.
• Opportunities to continuously learn and develop new AI skills.