Machine Learning Engineer (DE)

The role requires someone who can bridge data engineering and ML engineering by ensuring that high-quality, well-structured, scalable data flows are available for model training, inference, evaluation, and business reporting. Key Responsibilities Build, maintain, and optimize data pipelines that support ML/ Model workflows, Decision systems, model inference, and validation processes. Work with large-scale structured and semi-structured datasets across product catalogs, competitor data, customer data, match data, and validation data. Develop robust ETL / ELT workflows using Python, SQL, Spark or equivalent distributed processing frameworks. Support feature generation, data reconciliation, data quality checks, and data observability for ML systems. Collaborate with ML Engineers and Data Scientists to prepare clean, reliable datasets for model training, evaluation, and inference. Improve pipeline performance, scalability, reliability, and cost efficiency. Build reusable data processing components and automation utilities. Debug data discrepancies across upstream and downstream systems. Support operational reporting, dashboards, metric pipelines, and audit workflows. Work closely with engineering teams to productionize ML data flows and ensure smooth handoffs between data systems and ML systems. Required Skills Strong programming skills. Hands-on experience with data processing pipelines and large datasets. Good understanding of ETL / ELT design, data modeling, partitioning, incremental processing, and data validation. Experience with tools or platforms such as Spark, Airflow, AWS Glue, Apache Hudi, Databricks, BigQuery, Snowflake, Redshift, Athena, or similar technologies. Ability to debug data quality issues and trace data across systems. Experience building monitoring, reconciliation, or data quality frameworks. Good understanding of APIs, file formats, and data storage formats Strong with cloud platforms, preferably AWS and GCP. Basic understanding of ML workflows such as training data preparation, inference data generation, evaluation datasets, and feature engineering. Experience working with ML pipelines or MLOps workflows. Ability to write clean, modular, maintainable, and well-documented code. Good to Have Exposure to vector databases, embeddings, search systems, or retrieval-based systems. Experience with product catalog data, e-commerce data, taxonomy, attributes, or entity matching. Understanding of model evaluation datasets and metric generation.