BNP Paribas Fortis - Senior AI Machine Learning Engineer H/F/X

Description de l'entreprise

Willing to push the boundaries with us for a more sustainable world? Thanks to the daily commitment of our more than 11,000 employees, BNP Paribas Fortis aims to be a responsible and sustainable leader and the preferred partner of our clients over the long term. Being the bank for a changing world means accompanying them through the changes they experience on a daily basis to help them achieve their projects. It also means reconciling our customers' individual, collective and professional challenges to build a more sustainable world together. Our bank is changing, and so are our products and services. Do you want to work with others to create new solutions for our customers? To join small self-managed, multidisciplinary teams? To work in short iterations (sprints) to develop a Minimum Viable Product (MVP) quickly, then improve it based on feedback from other teams to arrive at an end product and reduce the time to market? Great! Because at BNP Paribas Fortis we work in Agile mode. We are convinced that this is the most effective and fastest way to carry out our projects and go further, to constantly offer innovative solutions. Are you ready to co-create with us innovative solutions for our clients? 3, 2, 1 sprint! Joining the BNP Paribas Fortis adventure means being part of a changing world.

Description de la fonction

Your job in a nutshell

As a Senior AI Machine Learning Engineer, you drive the industrialization of AI solutions, ensuring they are reliable, scalable, and aligned with technical best practices. You act as the key interface between Data Scientists and IT teams, bridging technical constraints (latency, volumetry, APIs) with business needs to deliver production-ready ML pipelines (ingestion, training, deployment).
Your role is strategic and operational: transforming prototypes into robust, high-performance AI services while anticipating technological advancements and business demands. You lead by example, promoting excellence in code quality, security, and MLOps across your squad and community of practice (Cop). Your expertise ensures seamless collaboration between architects, Data Scientists, and infrastructure teams for flawless production integration.

And in detail

• You facilitate the communication between Data Scientists and IT production with regards to the deployment of ML models, ensuring that models put in production are equipped with the appropriate data pipelines and monitoring.
• You work with the Data Scientists to define and develop the target solution with production constraints in mind. This allows to select the correct run infrastructure and serving model (e.g. data ingestion scheme, API synchronicity ...) to address the business requirements (real-time responses, processing volumetry ...).
• You contribute to the automation of the different elements of the ML pipeline in order to integrate and deploy them in the production environment (e.g. building Docker/VM images, prepare unitary, regression and integration tests ...).
• You support Data Scientists on the usage of the existing industrial solutions available to build and monitor AI services (i.e. the CI/CD tools).
• You support IT Production on the parameterization of the target environment.
• You ensure that the model runs without errors, is retrained if needed (incl. automatically) and is monitored both from the IT and the business perspective.
• You promote best practices: enforce code quality, security, and logging standards within your team and Cop, you mentor Junior engineers on MLOps fundamentals, Python best practices, and inter-team collaboration. You challenge and improve existing processes, proposing innovative solutions for continuous improvement.

Your talents and competences

• Fluent English is required. Dutch and/or French is a strong plus.
• Agile methodology: Sprint planning, workload estimation, and backlog prioritization.
• Minimum 4+ years of professional experience in:
  • Python development (advanced concepts: decorators, OOP, performance optimization).
  • Usage of Python package managers (Pip, uv) and experience in dependency management
  • Code Vulnerability management.
  • AI use cases deployment into complex infrastructures using ML engineering (MLOps, model versioning, CI/CD, drift monitoring)
  • CI/CD pipelines: Jenkins, GitLab CI/CD (advanced and optimized workflows, artifact management)
  • Linux/Cloud infrastructure:
    • Containerization : Docker, Kubernetes (orchestration, scaling, resource management)
    • Linux (Bash scripting, system administration, troubleshooting).
  • Database systems: PostgreSQL (query optimization, schema design).
  • Advanced logging & monitoring
  • Incident management: Debugging complex issues and implementing fixes.
• It is preferable you have experience with:
  • API design with Django framework
  • Data pipelines: ELT/ETL tools (Spark, Airflow), distributed ML systems.
  • Big Data tools: Spark (distributed...

...

Compétences linguistiques

• Anglais (atout)

  • Comprendre : Expérimenté - (C1)
  • Écrire : Expérimenté - (C1)
  • Lire : Expérimenté - (C1)
  • Parler : Expérimenté - (C1)

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