Data Engineer for AI/ML

1 month ago

India Dhira Full time

Responsibilities 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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