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 Responsibilities

Develop 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 integration

Required 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