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.