Senior Machine Learning Engineer / LLM / Gen AI (M/F)

CUSTOMER

In a context of company transformation, and to strengthen the current team, we are looking for several profiles :

• Senior Machine Learning Engineer (M/F)

We are looking for Freelance and CDI profiles with expertise in Machine Learning, MLOps and industrialization of AI models.
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MISSIONS

Machine Learning Engineers are responsible for the design, development and production of machine learning models designed to optimize and automate recruitment and talent management operations.

They will work on training, deploying and monitoring AI models, while ensuring their scalability and integration into our technological ecosystem.

Their responsibilities include :

• Development and optimization of AI models (LLMs, vision, NLP, recommendations).
• Implementation of MLOps best practices (CI/CD, monitoring, model orchestration).
• Design and maintenance of scalable and distributed architectures.
• Industrialization and deployment of models on cloud infrastructures(AWS, GCP, Azure).
• Collaboration with software and data teams to integrate models into our products.
• Continuous improvement in model performance and inference times.
• Technology watch and experimentation with new approaches to AI and Deep Learning.

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PROFILES
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• Experienced: Over 5 years' experience in Machine Learning and MLOps, with expertise in technologies such as Python, TensorFlow, PyTorch and Scikit-learn.
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• Leadership: ability to design and implement robust and scalable ML solutions.
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• Builder: Enthusiastic about laying the technical and methodological foundations in a fast-growing environment.
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• Passionate: genuinely motivated by the impact of AI in recruitment automation and large-scale talent management.
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• Curious: Enjoys discovering and experimenting with new technologies and machine learning models.
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• Committed: This position requires a strong commitment and a willingness to contribute to an ambitious mission.
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TECHNICAL STACK

• Development: Python, TensorFlow, PyTorch, Scikit-learn
• MLOps: CI/CD, monitoring, model orchestration (Kubeflow, MLflow, Airflow)
• Cloud: AWS, GCP, Azure (SageMaker, Vertex AI, Azure ML)
• Infrastructure: Docker, Kubernetes, GitHub Actions
• Data & APIs: Model industrialization, REST/GraphQL APIs, NoSQL/SQL databases
• Distributed systems: optimization and deployment of large-scale models
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TEAM

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PROCESS

1. Visio with the CTO
2. Technical testing (Pair Programming)
3. Visio with Co-Founder
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TERMS AND CONDITIONS

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