ML Engineer

About the Role

We need an ML engineer who ships models to production, not just notebooks. You will work on real business problems - demand forecasting, document intelligence, recommendation systems - and own the full lifecycle from data to deployment.

Responsibilities

• -Build and deploy machine learning models that solve specific business problems
• -Design and maintain data pipelines for training data preparation and feature engineering
• -Evaluate model performance rigorously and communicate results to non-technical stakeholders
• -Integrate ML capabilities into production applications via APIs
• -Monitor deployed models for drift and degradation
• -Document model logic, limitations, and handoff requirements clearly

Requirements

• -3+ years of experience building ML systems in Python
• -Proficiency with PyTorch or TensorFlow and scikit-learn
• -Strong understanding of model evaluation, cross-validation, and avoiding leakage
• -Experience deploying models via REST APIs (FastAPI or Flask)
• -Familiarity with experiment tracking tools (MLflow, W&B)
• -Comfortable with SQL and data wrangling at scale

Nice to Have

• ○Experience with LLMs and prompt engineering in production contexts
• ○Knowledge of recommendation systems or time-series forecasting
• ○MLOps experience (feature stores, model registries, CI for ML)
• ○Background in a domain vertical (fintech, healthcare, logistics)