Job Description
ML Engineer
Location: Hybrid
Employment Type: Full-time
Roles & Responsibilities
Model Development: Design, build, and deploy machine learning models across classical, time-series, and deep learning approaches to solve real business problems.
Data Pipelines: Build and maintain reliable data ingestion and transformation pipelines, ensuring data quality, consistency, and reproducibility.
Feature Engineering: Engineer high-quality features from structured and unstructured data, and manage them through feature stores for training and serving consistency.
Experimentation: Run rigorous experiments using proper validation strategies, perform hyperparameter tuning, and benchmark models against well-defined baselines.
Model Deployment: Package models into containerised services, expose them via APIs, and deploy to cloud ML platforms for scalable, low-latency inference.
MLOps & Automation: Implement CI/CD for ML, automated retraining workflows, model registry, and cham...
Location: Hybrid
Employment Type: Full-time
Roles & Responsibilities
Model Development: Design, build, and deploy machine learning models across classical, time-series, and deep learning approaches to solve real business problems.
Data Pipelines: Build and maintain reliable data ingestion and transformation pipelines, ensuring data quality, consistency, and reproducibility.
Feature Engineering: Engineer high-quality features from structured and unstructured data, and manage them through feature stores for training and serving consistency.
Experimentation: Run rigorous experiments using proper validation strategies, perform hyperparameter tuning, and benchmark models against well-defined baselines.
Model Deployment: Package models into containerised services, expose them via APIs, and deploy to cloud ML platforms for scalable, low-latency inference.
MLOps & Automation: Implement CI/CD for ML, automated retraining workflows, model registry, and cham...
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