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AI / ML Engineer

AI / ML Engineer

BayRock LabsAnand, IN
2 days ago
Job description

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
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    Engineer • Anand, IN

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