Machine Learning Engineer

Must Have Skills

• Strong understanding of Statistics, Probability, Linear Algebra, and ML fundamentals
• Hands-on experience with classical Machine Learning algorithms: Regression, Classification, Clustering, Recommendation Systems, and Time Series Forecasting
• Experience in feature engineering, data preprocessing, and model evaluation
• Understanding of model performance metrics (AUC, NDCG, precision/recall, F1) and validation techniques
• Experience in developing and deploying ML models into production environments
• Understanding of model observability, logging, monitoring, and debugging
• Proficient in Python with ability to write clean, production-ready code under time constraints. Candidates will be tested on live coding during the interview process.
• Proficient in SQL: window functions, CTEs, conditional aggregation, and analytical queries. Candidates will be tested on live SQL during the interview process.
• Familiarity with data pipeline orchestration tools (Airflow, Prefect, or similar)
• Exposure to cloud platforms and basic MLOps concepts
• Basic understanding of data engineering concepts like ETL design, Medallion architecture, etc.

Good-to-Have

• Experience with feature stores and online/offline feature serving
• Understanding of real-time model serving and latency-sensitive inference
• Experience with Databricks and Snowflake is a plus.
• Retail domain exposure
• Experience in Personalization and Recommendation use cases

What this role is NOT

• This is not a GenAI/LLM application developer role. Candidates whose primary experience is only building chatbots, RAG pipelines, or calling LLM APIs without classical ML depth will not meet the bar.