We are seeking a Machine Learning Engineer with a strong focus on MLOps to productionize and manage our core predictive models.

You will be the link between our Data Science and Production Engineering teams, ensuring our ML models are deployed and managed with high efficiency and reliability.

Key Responsibilities :

- Design and implement end-to-end MLOps pipelines for continuous integration, training, and deployment (CI/CT/CD) of models.

- Utilize MLOps tools like MLflow, Kubeflow, or TFX to manage model versioning, metadata, and reproducibility.

- Collaborate with Data Scientists to refactor prototype models (Python/PyTorch/TensorFlow) into scalable, production-ready microservices (e., using FastAPI).

- Develop and manage our Feature Store to ensure consistent and high-quality data access for both training and inference.

- Implement monitoring solutions for model performance, detecting data drift and model decay in real-time, and setting up automated retraining loops.

- Deploy and manage containerized ML inference services on Kubernetes clusters.

Technical Skills Required :

- Expert-level proficiency in Python and ML frameworks (PyTorch/TensorFlow).

- Hands-on experience with MLOps platforms (MLflow or Kubeflow).

- Proficiency with containerization (Docker) and orchestration (Kubernetes).

- Experience with cloud-based data and ML services (AWS SageMaker or GCP AI Platform).

- Strong knowledge of data warehousing, SQL, and big data processing (Apache Spark)