Embedded AI ML Engineer for Fintech Automation

PARTECH PARTNERS; France; Hybride; EUR 70 000 - 110 000; Plein temps

Résumé du poste

Pennylane is seeking a Machine Learning Engineer — Embedded AI to turn difficult accounting problems into trusted ML products used daily. You will design and ship ML systems for document understanding, extraction, classification, and matching, owning the full lifecycle from framing to deployment.

We value production-ready ML, strong Python, and hands-on experience with PyTorch, PySpark, Redshift, Airflow, SageMaker, and monitoring. Fluency in English; French is not mandatory.

Qualifications

• 5–8 years of experience and strong Python
• Hands-on experience building and operating ML systems at scale in production
• Ability to frame ambiguous product problems, select useful metrics and measure impact

Responsabilités

• Design and ship ML systems for document understanding, extraction, classification, matching, ranking, scoring and recommendations.
• Own the full lifecycle: problem framing, data strategy, baselines, training, deployment, monitoring and maintenance.
• Turn user corrections and production feedback into better datasets, models and product behavior.
• Define quality metrics reflecting user value: precision/recall, automation coverage, latency, cost.
• Collaborate with Product, Engineering and accounting experts to fit ML into user workflows.
• Choose simple, reliable approaches per problem: deterministic logic, classical ML, deep learning or generative AI.
• Improve ML engineering practices: reusable components, observability, data quality, training/inference pipelines.
• Stay ahead by monitoring emerging ML/AI techniques and apply when valuable.

Connaissances

Python

Production ML

Document AI

ML systems

NLP

Multimodal

Generative AI

Data quality

Outils

PyTorch

PySpark

Redshift

Airflow

AWS SageMaker

Description du poste

Pennylane is seeking a Machine Learning Engineer — Embedded AI to turn difficult accounting problems into trusted ML products used daily. You will design and ship ML systems for document understanding, extraction, classification, and matching, owning the full lifecycle from framing to deployment.

We value production-ready ML, strong Python, and hands-on experience with PyTorch, PySpark, Redshift, Airflow, SageMaker, and monitoring. Fluency in English; French is not mandatory.