Purpose of the Role
The AI & Data Engineer designs, builds, and operationalises advanced data and AI solutions within a regulated environment. The role supports transformation initiatives by enabling scalable analytics, machine learning, and LLM-based systems, ensuring robustness, performance, and compliance throughout the AI lifecycle.
Data Engineering & AI Development
Design and maintain data pipelines and data products for analytics and AI use cases.
Develop hybrid data architectures (on-premise / cloud).
Implement CI/CD automation, testing frameworks, and industrialised delivery processes.
Machine Learning & Advanced Analytics
Develop, deploy, and monitor machine learning models (e.g., fraud detection, AML/KYC, performance analytics).
Conduct experimentation and prototype evaluation using structured metrics.
Translate research into production-ready solutions and support code reviews.
AI & LLM Engineering
Build solutions for OCR, document classification, information extraction, and retrieval-augmented generation (RAG).
Design prompt templates, evaluation datasets, and LLM interaction workflows.
Integrate modern AI platforms and tools into operational processes.
Architecture & MLOps
Integrate AI models into business systems via APIs, microservices, and orchestration layers.
Ensure secure, production-grade deployments with monitoring, versioning, and governance.
Stakeholder Collaboration
Produce technical documentation and communicate insights to both technical and business stakeholders.
Support risk, compliance, and business teams to ensure responsible AI adoption.
Required
5+ years of experience in data engineering, data science, or AI roles within regulated environments.
Strong expertise in Python, SQL, Git, and modern data/AI tooling.
Experience designing large-scale data pipelines.
Solid knowledge of machine learning, NLP, vector search, and model evaluation.
Experience with platforms such as Dataiku, Snowflake, or other big data environments.
Fluency in French and English.
Preferred
Experience with LLM frameworks, prompt engineering, and document-intelligence workflows.
Knowledge of OCR technologies (e.g., Tesseract, OpenCV).
Exposure to MLOps/DevOps practices (CI/CD, APIs, containers).
Understanding of banking processes, AML/KYC, risk, and regulatory frameworks.
Master’s degree or higher in Computer Science, Data Science, Mathematics, or related fields.
5+ years of professional experience in data engineering, data science, or AI within regulated industries.
Relevant certifications (Dataiku, Snowflake, Python) are a plus.
Languages: Fluent French and English.
