About the Role

Join an ambitious engineering team focused on building high-impact machine learning infrastructure from the ground up. The mission of this project is to bridge the gap between data science and software engineering by taking models from Jupyter notebooks to production-grade APIs serving millions of predictions daily. You will work on critical features such as delivery time estimation, demand forecasting, and AI-powered insights.

The core engineering challenge lies in designing and managing the end-to-end MLOps lifecycle. You will build robust data pipelines, deploy models on major cloud platforms (GCP, AWS, or Azure), and create advanced monitoring systems to ensure continuous model performance and data quality.

Most Important

• 3+ years of professional experience with Python, with a proven track record of taking machine learning models from notebooks to production.
• 2+ years of experience building scalable backend services and APIs using TypeScript, JavaScript, or similar languages.
• Hands-on MLOps expertise, including deploying models, orchestrating CI/CD workflows, and establishing monitoring systems for model performance.
• Strong proficiency in data engineering workflows (real-time streaming, ETL/ELT, data quality validation) and advanced SQL optimization for large datasets.
• Practical experience working with major cloud platforms (GCP, AWS, or Azure).

Nice to Have

• Familiarity with streaming data platforms (e.g., Kafka) and container orchestration environments (Kubernetes).
• Experience with modern ML deployment platforms (e.g., serverless ML runtimes) or feature stores.
• Exposure to Elixir, functional programming concepts, or edge computing technologies.
• Domain knowledge in logistics, e-commerce, or supply chain systems.