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.

Skills

Machine Learning, Python, SQL, Algorithm, Generative AI, Data Pipeline