You will:

→ design and develop robust data pipelines and infrastructure to support efficient data collection, processing, and storage
→ collaborate with data engineers to design and optimize data architectures for ML applications, ensuring scalability, reliability, and performance
→ implement data preprocessing, transformation, and feature engineering techniques to prepare high-quality datasets for ML model training
→ develop APIs and backend services for seamless integration of ML models into production systems, ensuring data flow, model serving, and real-time inference capabilities
→ work closely with software engineers to design and implement scalable and secure RESTful APIs to expose ML functionalities to frontend applications and external partners
→ collaborate with R&D team to understand business requirements and translate them into data engineering and backend development tasks
→ conduct thorough testing, debugging, and troubleshooting of data pipelines, backend services, and APIs to ensure their reliability and accuracy
→ monitor and optimize data processing and API performance to ensure efficient and timely delivery of ML predictions and results
→ stay up-to-date with industry best practices in data engineering and backend development, continuously improving data processing pipelines, APIs, and infrastructure
→ document and communicate technical designs, processes, and API specifications to enable seamless collaboration and knowledge sharing within the team.

about you:

→ Bachelor's or higher degree in Computer Science, Engineering, or a related field
→ strong proficiency in Python programming and experience with ML libraries/frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
→ understanding of ML concepts, algorithms, and techniques
→ experience in data preprocessing, feature engineering, and working with large-scale datasets
→ proficiency in implementing and deploying ML models in production environments
→ strong data engineering skills, including experience in designing and developing datapipelines, data architectures, and efficient data processing and storage solutions
→ experience in API development and backend services, with a focus on creating robust, scalable, and secure APIs for integration with ML models and frontend applications
→ experience with Docker
→ CI/CD concept understanding
→ familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in deploying ML solutions using cloud services
→ knowledge of database systems, SQL, and NoSQL databases for efficient data storage and retrieval
→ strong problem-solving skills and the ability to analyze complex data engineering and backend development challenges
→ excellent communication and collaboration skills, with the ability to work effectively in a team environment
→ a strong drive for learning and staying updated with the latest trends and advancements in ML, data engineering, and backend development.