JobTeaser
Staffing And Recruiting
MachineLearningEngineer(W/M/D)
“Machine Learning Engineer (W/M/D) at JobTeaser. Skills: Machine Learning, AI, LLMs, Python, ML/LLMOps, Cloud Platforms, Data Pipelines. Design, develop, and deploy end-to-end ML and AI pipelines. Build and maintain robust data and ML pipelines for model training, evaluation, and deployment”
What You'll Achieve.
Turn prototypes into scalable, reliable production solutions; Apply emerging AI technologies to real-world use cases
Industry & Context.
Analytical mindset; Identify modeling challenges; Propose solutions
What They're Looking For.
Must Have
Master's degree (or equivalent) in Computer Science, Applied Mathematics, Data Science, or a related quantitative field, 3 to 5 years of experience developing and deploying Machine Learning models in production environments, Proficient in Python, Hands-on experience with major ML/AI frameworks (scikit-learn, XGBoost, LightGBM, HuggingFace Transformers, DSPy…), Solid knowledge of ML/LLMOps tools (MLflow, Kubeflow, DVC, DeepEval or similar) and ML-oriented DevOps practices, Experience with cloud platforms (AWS SageMaker, Azure ML, or GCP Vertex AI), Experience with containerization (Docker, Kubernetes), Comfortable with SQL, Knowledgeable in feature engineering and data preprocessing best practices, Pragmatic, analytical, and results-oriented mindset, Ability to translate business needs into concrete ML/AI solutions, Autonomous, curious, Professional proficiency in English, Bilingual in French
Nice to Have
LLM-powered features including RAG, structured prompting, few-shot learning, and OCR integration, ML/LLMOps practices: model versioning, production monitoring, reproducibility, and continuous improvement, APIs and microservices to expose ML/AI models to business applications, Analyze large datasets, identify modeling challenges, propose solutions, and iterate quickly, Stay current on academic and industrial advances in Machine Learning, LLMs and emerging AI technologies and apply them to real-world use cases, Contribute to best practices in testing, CI/CD, and deployment automation, Enjoy working in cross-functional teams (Data, Tech, Product), Comfortable in a fast-evolving ecosystem
What You'll Do.
and deploy end-to-end ML and AI pipelines
Build and maintain robust data and ML pipelines for model training
Develop LLM-powered features including RAG
Implement and evolve ML/LLMOps practices
Develop APIs and microservices to expose ML/AI models to business applications
Analyze large datasets
identify modeling challenges
Stay current on academic and industrial advances in Machine Learning
LLMs and emerging AI technologies and apply them to real-world use cases
How You'll Work.
Team & Collaboration
Collaborate closely with Data, Product, and Engineering teams to turn prototypes into scalable, reliable production solutions; Enjoy working in cross-functional teams (Data, Tech, Product)
Communication Scope
Professional proficiency in English; Bilingual in French
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