Description

You will lead the end-to-end machine learning lifecycle, from algorithm selection and feature engineering to production deployment and monitoring.

This role is on-site.

Responsibilities

• Develop and train machine learning models, performing feature engineering and hyperparameter tuning.
• Deploy scalable models into production environments and develop APIs to expose them as services.
• Monitor deployed models in real-time to track latency, throughput, and accuracy, addressing model and data drift.
• Manage infrastructure for training and deployment, including cloud platforms, GPUs, and distributed computing resources.
• Automate data preprocessing, training, and deployment workflows using Python and orchestration tools.

Required Skills

• 5+ years of professional experience in software engineering or machine learning.
• Proficiency in Python, SQL, Java, or Scala.
• Experience with ML frameworks including TensorFlow, PyTorch, scikit-learn, and Hugging Face Transformers.
• Hands-on experience with data manipulation libraries such as NumPy, Pandas, OpenCV, SpaCy, and NLTK.
• Practical knowledge of Docker, Kubernetes, and Azure Kubernetes Service (AKS).
• Experience building and managing CI/CD pipelines using Jenkins and Git.
• Proficiency with cloud technologies including Azure ML, Databricks, Snowflake, and Palantir Foundry.
• Ability to implement shell-scripting for task automation.
• Understanding of model evaluation metrics including precision, recall, F1 score, and AUC-ROC.

Key Skills

Education

Any Gradute