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Senior Data Engineer (ML) at Weekday AI. This role is for one of the Weekday's clients. Min Experience: 5 years. JobType: full-time. We are seeking a . Senior Data Engineer (ML Platform). to help design and build a no-code interface that enables users to develop predictive models for complex supply chain challenges. In this role, you will work closely with platform and data science teams to orchestrate end-to-end workflows for supervised learning and time-series forecasting, ensuring a seamless and high-performance user experience.. Why This Role Now:. The ML workspaces are being re-architected to efficiently manage large-scale data volumes while optimizing both system throughput and predictive performance. This involves enhancing ETL pipelines, increasing parallel processing efficiency, and creating a unified workflow that supports powerful yet user-friendly machine learning operations.. What Success Looks Like in the First 3–6 Months:. Optimize ETL and data storage pipelines to handle large-scale datasets (up to 25 million time series). . Work with a modern technology stack leveraging distributed computing, advanced ML algorithms, and MLOps tools. . Gain end-to-end exposure across the ML product lifecycle — from data ingestion to analytics and user-facing dashboards. . Take ownership of the ML workspace platform to drive continuous improvement and deliver exceptional user experience. . What Makes This Role Exciting:. You’ll contribute to developing one of the most advanced products in the platform ecosystem, shaping how users interact with ML workspaces and helping simplify predictive modeling for real-world business problems.. Key Responsibilities:. Apply best practices in software engineering and agile methodologies to build high-quality, scalable data solutions. . Research and prototype new frameworks and technologies to enhance the data and ML infrastructure. . Own and evolve the product architecture, ensuring continuous improvement and technical excellence. . Design and implement robust data pipelines to support data processing and analytical workflows. . Ensure data quality and consistency through effective cleaning, transformation, and validation. . Manage and optimize data flow lifecycles using domain-specific data schemas and efficient data storage techniques (SQL and NoSQL). . Collaborate with cross-functional teams to integrate, deploy, and maintain distributed data systems. . Must-Haves:. 5–10 years of experience in product or data engineering roles. . Proven expertise in designing and implementing data pipelines and ETL workflows. . Strong proficiency in Python, SQL, and REST API development. . Hands-on experience with modern data tools such as Airflow, Kafka, and Snowflake. . Solid understanding of distributed computing frameworks (Spark, Dask, Ray, etc.). . Familiarity with Docker, Kubernetes, and CI/CD pipelines. . Strong grounding in software engineering fundamentals and data modeling. . Nice-to-Haves:. Exposure to MLOps practices and tools, including monitoring and managing data drift. . Understanding of the end-to-end lifecycle of machine learning projects.. Company Location: India.