Must Have Skills
Robust 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.
Positive-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.

📌 Machine Learning Engineer Bengaluru (India)
🏢 Recruise India Consulting
📍 India