ML Engineer

Bachelor's/Master's degree in Computer Science, Statistics, Mathematics, or a related field (or equivalent experience).

Proven experience with Python programming & PySpark for data science and machine learning.

Experience with DevOps Concepts CI/CD and Shell Scripting

Experience with container orchestration platforms like Kubernetes and  containerization technologies like Docker.

Experience with cloud deployment (e.g., AWS, GCP, Azure).

Good understanding of machine learning algorithms and concepts.

Design, develop, and deploy production-ready ML models using Python libraries (e.g., Scikit-learn, TensorFlow, PyTorch) and big data frameworks (e.g., Spark).

Manage the deployment of ML models to the cloud environment and optimize the performance of ML Models.

Collaborate with data scientists, software engineers, and product managers to ensure successful implementation of ML solutions.

Leverage Docker and Kubernetes to containerize ML models for scalability and portability.

Stay up-to-date on the latest advancements in MLOPS, data science and machine learning.