Key Responsibilities
Design, train, and optimize machine learning and deep learning models for real-world applications.
Build and maintain data preprocessing, transformation, and feature engineering pipelines.
Deploy ML models to production using cloud services and MLOps best practices.
Monitor model performance, retrain when necessary, and improve accuracy and latency.
Work with APIs, microservices, and applications to integrate ML models into production systems.
Partner with data scientists to translate research into production-ready solutions.
Conduct experiments to evaluate algorithms and architectures for optimal results.
Maintain clear technical documentation for models, pipelines, and deployment processes.
Required Skills
Proficiency in Python (TensorFlow, PyTorch, Scikit-learn) and experience with Java, C++, or Go is a plus.
Solid understanding of algorithms, deep learning architectures (CNNs, RNNs, Transformers), and NLP techniques.
Experience with SQL/NoSQL databases and big data tools (Spark, Hadoop).
Proficiency with Docker, Kubernetes, MLflow, and model deployment pipelines.
Experience with AWS SageMaker, Azure ML, or Google Vertex AI.