Informacje o stanowisku

technologies-expected :

• Python
• Grafana
• OpenTelemetry
• PyTorch
• MLops
• Docker
• LangChain

• Docker

• You will join the Team, part of a strategic initiative to build a reusable, scalable Python library designed to support business-critical time series forecasting. This project is embedded within a broader ecosystem of AI & Analytics platforms focusing on observability, knowledge access, intelligent automation, and data governance.
• Team combines traditional statistical forecasting, machine learning, and deep learning approaches (e.g., scikit-learn, statsmodels, statsforecast, neuralforecast, PyTorch). The roadmap includes incorporating LLM-based forecasting capabilities. The work involves close collaboration with cross-functional teams, contributing to platform components that support a wide range of enterprise applications.
• You will also be involved in other strategic areas such as:
• Grafana + OpenTelemetry for full observability and user behavior tracking
• Sinequa Enterprise Search for knowledge centralization
• AI/ML tooling (e.g., transformers, scikit-learn) for behavior modeling and segmentation
• AI agents (LangChain, CrewAI) for intelligent automation
• Data Governance & FAIR principles (DataHub, Collibra)
• Data storytelling and visualization (Grafana, Streamlit, Power BI)

• Develop and maintain reusable Python-based forecasting components using object-oriented programming
• Work across the full machine learning lifecycle: from data preparation and modeling to productization and operationalization
• Apply best practices in code design, testing (unit/integration), and DevOps/CI/CD workflows
• Support implementation of new methodologies including LLMs for forecasting
• Collaborate with platform and data engineering teams to ensure scalable deployment
• Contribute to internal knowledge sharing and documentation

• 3+ years of professional experience in Python-based machine learning projects
• Solid understanding of machine learning algorithms (supervised learning, hybrid models, overfitting, etc.)
• Proven ability to write clean, efficient, object-oriented Python code
• Practical experience with model evaluation and testing techniques
• Basic understanding of time series analysis principles
• Experience with structuring ML solutions into scalable software packages
• Exposure to or interest in time series forecasting libraries/methods
• Familiarity with deep learning libraries relevant to forecasting (e.g., PyTorch, neuralforecast)
• Understanding of CI/CD pipelines, unit testing, and MLOps principles

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