You will work cross-functionally with both technical and business teams to craft tailored, high-impact AI solutions that drive real-world value.
Responsibilities
On the Business Side
- Partner with clients and internal stakeholders to understand strategic objectives and uncover ML/AI opportunities.
- Assess feasibility by analyzing available data sources and proposing realistic, impactful solutions.
- Define project goals, success criteria, and measurable KPIs in alignment with business needs.
- Lead the design and development of machine learning models using PyTorch, TensorFlow, or similar frameworks.
- Implement best-in-class MLOps practices to automate training, deployment, and monitoring of models (e.g., with Kubeflow).
- Build and optimize scalable pipelines for data ingestion, model training, evaluation, and inference.
- Ensure full automation from preprocessing to production, with robust testing and CI/CD integration.
- Collaborate closely with data engineers and platform teams to ensure model performance, reliability, and maintainability.
- 5+ years of hands-on experience in ML/AI engineering or data science roles.
- 3+ years of experience with Google Cloud Platform (GCP), including hands-on use of Vertex AI and BigQuery.
- Proven experience delivering production-grade models and scalable ML systems.
- Strong knowledge of ML algorithms, model development, deployment, and lifecycle management.
- Proficiency in Python and ML frameworks such as TensorFlow or PyTorch.
- Expertise in MLOps tooling and automation (e.g., Kubeflow, MLflow, Vertex AI Pipelines).
- Experience applying LLMs, especially in Retrieval Augmented Generation (RAG) workflows.
- Familiarity with data preprocessing, model tuning, and performance optimization.
- Clear communicator with the ability to translate complex ideas to technical and non-technical audiences.
- Fluent in English (French or Spanish is a plus).
- Eligible to work in Europe.
- Experience with LangChain, LangGraph, or dbt.
- Prior involvement in consulting or client-facing AI/ML projects.
- Contribution to open-source or published work in AI/ML communities.
- Generosity – giving back through open-source and community initiatives.
- Transparency – every voice matters; decisions are shared and aligned.
- Growth – continuous learning and experimentation encouraged.
- Ownership – responsibility and impact are celebrated.
- Trust – people are valued as individuals, not just colleagues.
- Ever-learning – time is dedicated to innovation, training, and knowledge-sharing.