Senior/Staff Machine Learning Engineer (MLOps)

Location: Paris, France; hybrid model with typically 2 days remote per week

Company

hirify.global operates a global fashion marketplace focused on circularity, luxury resale, authentication, and counterfeit detection.

What you will do

• Build foundational MLOps infrastructure and establish the ML engineering strategy across the marketplace.
• Productionize and scale computer vision, VLM, and RAG models for luxury product authentication and fraud detection.
• Design systems for data and feature management, model tracking and registries, model serving, and monitoring.
• Automate continuous retraining pipelines with deployment cadences ranging from daily fraud detection to weekly recommendations.
• Design resilient decoupled multi-model architectures and evaluate in-house tooling against enterprise platforms using technical overhead and total cost of ownership.
• Provide horizontal ML infrastructure support across Search, Discovery, Pricing, Marketing, and Data Platforms while mentoring the ML engineering organization.

Requirements

• 5–8+ years of hands-on Machine Learning Engineering experience focused on MLOps infrastructure and production ML systems.
• Experience deploying low-latency, high-throughput inference services with FastAPI, TorchServe, Triton Inference Server, or Ray Serve.
• Strong experience with PyTorch or TensorFlow, AWS services such as EKS, EC2, or SageMaker, Snowflake, and open-source ML ecosystems.
• Experience building continuous retraining pipelines and multi-model architectures with Airflow, Kubeflow, or Metaflow, plus MLflow or Weights & Biases.
• Production experience with online and offline feature stores, including Redis, DynamoDB, Snowflake, S3, Feast, or dbt-based pipelines.
• Strong engineering practices covering version control, testing, CI/CD, scalable architecture, and cross-functional collaboration with data science and backend engineering.

Nice to have

• Experience in e-commerce, single-SKU marketplaces, search and recommendation, trust and safety, or counterfeit detection.
• Experience with vector databases, Visual RAG, deep learning VLMs, ONNX, TensorRT, or edge optimization.
• Advanced experience with Docker, Kubernetes, Terraform, dbt, Datadog, or Prometheus.

Culture & Benefits

• Purpose-driven work focused on circular fashion, reducing waste, and extending the life of luxury items.
• Products used globally by millions of users across more than 70 countries.
• International environment with colleagues from more than 50 nationalities.
• Learning budget, continuous feedback, and opportunities to work on AI, marketplace dynamics, and scalability.
• Bonus, health coverage, lunch vouchers, Gym-Pass, and additional legal benefits depending on location.
• Two paid days per year to support a cause of choice.