Machine Learning Engineer Expert

About the job Job purpose

We are seeking an exceptional Senior Machine Learning Engineer to join our advanced AI lab at the EPFL Innovation Park in Lausanne. This role is ideal for a hands‑on ML engineer with deep expertise in computer vision, geospatial intelligence, and production ML systems who wants to shape the future of AI‑driven risk assessment in the insurance industry.

You will lead the development and deployment of cutting‑edge computer vision and geospatial AI systems that analyze aerial imagery at scale, transforming how AXA assesses and understands risk globally. This position requires someone who excels at both research and production, with a proven track record of building ML systems from conception to large‑scale deployment.

Main missions

Your responsibilities include:

ML Systems Architecture & Engineering

• Design and implement scalable ML pipelines for processing aerial images and geospatial data at scale
• Build production‑grade computer vision systems for detecting risk indicators (solar panels, roof conditions, industrial equipment) from satellite and drone imagery
• Architect cloud‑native ML solutions on AWS using Sage Maker, Step Functions, Lambda, and infrastructure as code (Terraform)
• Develop real‑time and batch inference systems handling large amount of geospatial data
• Implement MLOps best practices including model versioning, monitoring, and automated retraining pipelines

Computer Vision & Deep Learning Development

• Lead research and development of state‑of‑the‑art computer vision models for risk detection
• Fine‑tune and optimize large‑scale vision models (SAM, DINO, Vision Transformers) for geospatial applications
• Implement custom deep learning architectures using PyTorch/Tensor Flow for specialized detection tasks
• Develop multi‑modal AI systems combining imagery, geospatial features, and structured data
• Build and maintain model evaluation frameworks with comprehensive metrics and visualization tools

Generative AI & LLM Integration

• Design and deploy LLM‑powered solutions using Lang Chain, RAG architectures, and vector databases
• Implement geospatial‑aware LLMs (GeoLLMs) for intelligent risk analysis and reporting
• Build conversational AI interfaces for querying and analyzing geospatial risk data
• Develop prompt engineering strategies and fine‑tuning pipelines for domain‑specific applications

Data Engineering & Pipeline Development

• Build robust data pipelines for ingesting, processing, and storing large‑scale geospatial datasets
• Implement distributed computing solutions using PySpark, Dask, or similar frameworks
• Design data versioning and lineage tracking systems for ML reproducibility
• Optimize data storage and retrieval for large imagery archives
• Develop automated data quality monitoring and validation systems

Production Deployment & Dev Ops

• Containerize ML applications using Docker and deploy to Kubernetes clusters
• Implement CI/CD pipelines with Git Hub Actions/Git Lab CI for automated testing and deployment
• Build microservices architectures using FastAPI/Flask for model serving
• Set up monitoring, logging, and alerting systems for production ML services
• Ensure system reliability with 99.9%+ uptime for critical risk assessment services

Technical Leadership & Collaboration

• Mentor junior engineers and data scientists on ML engineering best practices
• Coordinate annotation campaigns and model development with global teams
• Collaborate with insurance underwriters and risk consultants to translate business needs into technical solutions
• Lead technical design reviews and architecture decisions for ML systems
• Contribute to open‑source projects and represent AXA at technical conferences

Expected skills & experience Experience

• Bachelor's or Master's degree in Computer Science, Engineering, or related technical field
• 7+ years of hands‑on experience in machine learning engineering and data science
• 3+ years specifically in computer vision and deep learning applications
• Proven track record of deploying ML models to production at scale
• Experience with geospatial data processing and analysis

Technical skills

• Expert‑level Python programming with strong software engineering practices
• Deep expertise in ML frameworks:
  PyTorch, Tensor Flow,…