Manager AI ML Engineering

Department Overview

We are looking for a Machine Learning Engineer to develop, train, and deploy machine learning models, ensuring they are accurate, scalable, and production-ready. The ideal candidate has strong hands-on experience in ML model development, deployment, and MLOps practices, with familiarity in data exploration and feature engineering.

Key Responsibilities:

• Develop, train, and evaluate machine learning models, including feature engineering, model selection, and hyperparameter tuning.
• Deploy ML models into production environments, ensuring scalability, reliability, and maintainability.
• Design, implement, and maintain model deployment pipelines, including CI/CD workflows, automated testing, and model versioning.
• Develop and maintain monitoring systems to track model performance, data drift, and system health.
• Build backend services and APIs to integrate ML models with product systems and applications.
• Apply best practices in MLOps, ensuring reproducibility, reliability, and efficient model operations.
• Collaborate with engineers and product teams to deliver high-quality ML solutions.

Qualifications

• Master’s degree in computer science, Machine Learning, Data Science, or a related field.
• 5+ years of experience in ML engineering or related roles.
• Proven experience in training, developing, and deploying ML models.
• Familiarity with data exploration, preprocessing, and feature engineering.
• Proficiency in Python and standard ML/data science libraries (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, etc.).
• Experience with API development and backend integration (e.g., FastAPI, Flask, Django,).
• Strong understanding of MLOps tools and practices, such as MLflow, Kubeflow, Airflow, Prefect, Docker, Kubernetes.
• Experience building data pipelines, processing workflows, and monitoring deployed models.

Preferred:

• Experience with distributed computing frameworks (Spark, Ray).
• Familiarity with cloud platforms (AWS, GCP) or platform-agnostic orchestration tools.
• Experience working in fast-paced, experimentation-driven environments.
• Knowledge of microservices or event-driven architectures.