Data Engineer for AI/ML
4 days ago
- Develop and maintain APIs using Flask and Fast API frameworks to support AI/ML services, including data access, transformation, and integration with Large Language Models (LLMs).- Design and implement ETL processes to manage data pipelines and build Data Warehouses (DW) for efficient data storage and retrieval.- Work on integrating LLMs with API services, ensuring secure and efficient data flow and model interaction.- Develop APIs that abstract and conceal LLM functionality, providing a seamless interface for applications to interact with AI/ML models.- Collaborate with database systems to handle data manipulation, storage, and retrieval, supporting API-driven machine learning workflows.- Optimize Python code for performance and security, ensuring robust and scalable API deployment.- Participate in cross-functional team discussions to align technical solutions with business objectives.- Stay abreast of advancements in AI, machine learning, and software development practices to suggest and implement improvements.- Research and develop new algorithms to improve AI system performance.- Collaborate with cross-functional teams to integrate AI models and technologies into scalable products.
Key Qualifications
- Strong proficiency in Python with 6 years of experience in developing APIs.- Expertise in Flask and Fast API for API development.- Solid understanding of ETL processes, Data Warehousing, and working with relational databases such as PostgreSQL or MySQL.- Experience with integrating and managing Large Language Models (LLMs) and concealing their APIs behind custom-built services.- Knowledge of data transformation and access techniques to effectively feed AI/ML models.- Familiarity with Machine Learning development is advantageous but not essential.
Desired Traits
- Strong problem-solving skills with a focus on optimizing API performance and ensuring security.- Ability to work both independently and collaboratively within a team.- Effective communication skills to explain technical concepts clearly and concisely.- Cloud computing: Familiarity with cloud platforms such as AWS, Azure, or GCP.- Big data technologies: Experience with big data tools and technologies such as Spark or similar.- Natural language processing (NLP): Knowledge of NLP techniques and applications.- Computer vision: Understanding of computer vision algorithms and applications
Educational Background
Bachelor’s degree in computer science, Information Technology, or a related field.
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