Machine Learning Engineer
MARGO · Paris, Île-de-France, France
- On site
- Contract
- $90,000 / year
- Paris, Île-de-France, France
Job highlights
- Industrialize ML algorithms and manage their lifecycle.
- Develop robust, tested, and optimized production code.
- Design and maintain MLOps pipelines and CI/CD.
- Optimize model performance and resource utilization.
- Collaborate between Data Science and Data Engineering teams.
About the role
About MARGO
At MARGO, our consultants work on what truly matters: complex projects that combine intellectual challenge and real business impact. This is why we support major players in finance, industry, and technology on their most strategic projects in Data Science, Machine Learning, and Artificial Intelligence.
Why join the AI practice?
You will work within a team led by Hamza Bouanani, AI Practice Manager at MARGO and Lead Data Scientist at BNP Paribas. Working alongside him means joining a team of passionate experts, being technically and methodologically challenged, and contributing to high-impact projects for clients.
Your missions
We are looking for a Machine Learning Engineer to join our teams and work at the heart of our clients' Data strategy (Industry, Finance, Energy). Your main objective will be to industrialize Machine Learning algorithms and ensure their production lifecycle.
You will be involved in three major technical areas:
1. Industrialization & Deployment (Model Serving)
- Transforming research models (Proof of Concept) into robust, tested, and optimized production code.
- Developing high-performance APIs (FastAPI, Flask) to expose models to business applications.
- Containerizing solutions (Docker) and orchestrating them on clusters (Kubernetes) to ensure scalability and high availability.
2. MLOps Architecture & Automation
- Designing and maintaining CI/CD pipelines dedicated to Machine Learning (automatic retraining, model validation).
- Implementing and managing data flow orchestration tools (Airflow, Kubeflow, Dagster).
- Managing data and model versioning (DVC, MLflow) to ensure experiment reproducibility.
3. Performance & Optimization
- Optimizing response times (latency) and computational resource usage (CPU/GPU).
- Refactoring code to adhere to Software Craftsmanship standards (Clean Code, TDD).
- Managing Feature Stores to centralize and serve real-time calculated variables.
Your role will also include:
- Advanced Monitoring: Implementing probes to detect "Data Drift" or "Model Drift" and trigger proactive alerts.
- Cross-functional Collaboration: Bridging the gap between Data Scientists (mathematics/modeling) and Data Engineers (infrastructure/data) to streamline production deployments.
- Evangelism: Disseminating good software development practices within Data Science teams.
Profile sought
We are looking for a hybrid profile, at the intersection of Software Engineering and Data Science, capable of understanding the underlying mathematics while mastering IT production constraints.
- Education: Master's degree (engineering school, university) in Computer Science or Applied Mathematics.
- Core Technical Skills:
- Advanced proficiency in Python and software best practices (Git, Unit/Integration Tests, Packaging).
- Solid experience with Cloud platforms (AWS, Azure, or GCP) and IaC (Terraform is a plus).
- Mastery of the MLOps ecosystem: MLflow, Kubeflow, Docker, Kubernetes.
- Data Knowledge: Good understanding of ML libraries (Scikit-learn, Pandas) and Deep Learning (TensorFlow, PyTorch) to optimize Data Scientists' code.
- Experience: Significant experience in deploying ML models in production (batch or real-time).
- Mindset:
- Absolute rigor regarding code quality and automation.
- A knack for solving complex architectural problems.
- Ability to work in heterogeneous technical environments.
What we offer
- Ambitious and varied missions, always selected for their added value.
- Close support from recognized experts.
- A community of passionate engineers, with workshops, conferences, and regular exchanges.
- A culture of technical excellence, knowledge sharing, and continuous development.
COMMITMENT TO INCLUSION
MARGO is committed to offering equal opportunities to all; we foster an inclusive work environment that values diversity.
Key skills/competency
- Machine Learning Engineer
- Model Deployment
- MLOps
- Python
- Docker
- Kubernetes
- Cloud Platforms (AWS, Azure, GCP)
- API Development
- CI/CD
- Data Drift
Skills & topics
- Machine Learning Engineer
- MLOps
- Python
- Docker
- Kubernetes
- Cloud
- AWS
- Azure
- GCP
- API Development
- CI/CD
- Data Science
- Software Engineering
- Production Deployment
- Model Serving
- Data Drift
- Model Drift
- FastAPI
- Flask
- Airflow
- Kubeflow
- DVC
- MLflow
- Terraform
How to get hired
- Tailor your resume: Highlight Python, MLOps tools (MLflow, Kubeflow), and cloud platform experience.
- Showcase production experience: Detail your role in deploying ML models in real-time or batch.
- Demonstrate hybrid skills: Emphasize your ability to bridge Software Engineering and Data Science.
- Prepare for technical questions: Be ready to discuss algorithm industrialization, API development, and CI/CD pipelines.
- Understand MARGO's culture: Research their focus on complex projects and technical excellence.
Technical preparation
Master Python and software best practices.,Gain experience with MLOps tools like MLflow, Kubeflow.,Practice containerization with Docker and Kubernetes.,Build and deploy APIs using FastAPI or Flask.
Behavioral questions
Describe a complex architecture problem solved.,How do you ensure code quality and automation?,How do you bridge Data Science and Data Engineering?,How do you handle monitoring and alerts for drift?
Frequently asked questions
- What is the primary focus of a Machine Learning Engineer at MARGO?
- The primary focus of a Machine Learning Engineer at MARGO is to industrialize Machine Learning algorithms and manage their entire lifecycle in production environments for clients in industries like finance, industry, and energy.
- What are the key technical responsibilities for this Machine Learning Engineer role?
- Key responsibilities include transforming Proof of Concepts into production-ready code, developing performant APIs, containerizing solutions with Docker, orchestrating with Kubernetes, designing MLOps pipelines, automating retraining, managing data/model versioning, and optimizing performance.
- What programming languages and tools are essential for this position?
- Essential tools include advanced Python proficiency, Git, testing frameworks, and expertise in MLOps ecosystem tools such as MLflow, Kubeflow, Docker, and Kubernetes. Experience with FastAPI or Flask for API development is also crucial.
- What level of experience is required for the Machine Learning Engineer role at MARGO?
- The role requires significant experience in deploying ML models into production, whether in batch or real-time scenarios. A Master's degree in Computer Science or Applied Mathematics is also a prerequisite.
- Does MARGO offer opportunities for continuous learning and development for its Machine Learning Engineers?
- Yes, MARGO emphasizes continuous development through a culture of technical excellence, knowledge sharing, and regular exchanges within a community of passionate engineers, including workshops and conferences.
- What is the work environment like for an AI Practice member at MARGO?
- You will work within a team of passionate experts led by an experienced Practice Manager, facing technical and methodological challenges on high-impact client projects in a collaborative and inclusive environment.
- What are the expectations regarding cloud platform and IaC skills for this role?
- Solid experience with cloud platforms like AWS, Azure, or GCP is expected. Proficiency in Infrastructure as Code (IaC) tools like Terraform is considered a strong advantage for this Machine Learning Engineer position.
- How does MARGO ensure quality and automation in its Machine Learning projects?