AI / ML Engineer

Role Overview : Build, train, and deploy machine learning models for predictive analytics and data-driven decision making. Implement end-to-end ML pipelines from data preparation to production deployment.Key ResponsibilitiesDevelop and train ML models for classification, regression, forecasting, and anomaly detection, Perform feature engineering, data preprocessing, and exploratory data analysis

• Implement model training pipelines with hyperparameter optimization
• Deploy models to production and integrate with application services
• Monitor model performance, detect drift, and trigger retraining
• Collaborate with data engineers on feature store and data pipeline design
• Conduct A / B testing and model performance evaluation
• Document model architectures, experiments, and deployment processes Required Skills Machine Learning :
• Strong foundation in supervised and unsupervised learning algorithms
• Time-series forecasting and anomaly detection techniques
• Classification, regression, clustering, and ensemble methods
• Feature engineering and feature selection strategies
• Model evaluation metrics and validation techniques
• Handling imbalanced datasets and data quality issues Statistical & Mathematical :
• Statistics, probability, and linear algebra
• Hypothesis testing and statistical inference
• Optimization algorithms and gradient descent
• Understanding of model bias, variance, and overfitting Data Processing :
• Data cleaning, transformation, and normalization
• Exploratory Data Analysis (EDA) and data visualization
• Working with structured and unstructured data
• ETL / ELT pipeline integrationRequired Tech Stack Programming & ML :
• Languages : Python (expert), SQL
• ML Libraries : Scikit-learn, XGBoost, LightGBM, CatBoost
• Deep Learning : PyTorch or TensorFlow, Keras
• Data Processing : Pandas, NumPy, Polars
• Visualization : Matplotlib, Seaborn, Plotly MLOps & Deployment :
• Experiment Tracking : MLflow, Weights & Biases
• Model Serving : FastAPI, Flask, TensorFlow Serving
• Containerization : Docker
• Version Control : Git, DVC (Data Version Control)
• Workflow : Airflow, Prefect Cloud & Tools :
• Cloud Platforms : AWS (SageMaker), Azure ML, or GCP (Vertex AI)
• Databases : SQL (PostgreSQL, MySQL), NoSQL basics
• Tools : Jupyter, VS Code, Linux / Unix Preferred Qualifications
• Bachelor's / Master's in Computer Science, Data Science, Statistics, or related field
• Experience with distributed training (Spark MLlib, Ray)
• Knowledge of AutoML and hyperparameter tuning frameworks (Optuna, Hyperopt)
• Kaggle competitions or ML portfolio projects What Success Looks Like
• Production models achieving target accuracy and business KPIs
• Automated ML pipelines reducing manual intervention
• Fast iteration cycles for model experimentation
• Well-documented, maintainable code and models
• Collaboration with cross-functional teams