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