Backend Ai Engineer and Data Architect

We are seeking a skilled Backend AI Engineer and Data Architect,  professional in systems engineering, computer science, artificial intelligence, or related areas, with at least 2 years of proven experience in backend development and projects related to artificial intelligence, especially in natural language processing (NLP).

The ideal candidate must have solid knowledge of data architecture, scalable data design, and modeling, as well as experience implementing Retrieval-Augmented Generation (RAG), which is essential for integrating language models with information retrieval systems. This role requires advanced skills in backend development, data architecture, data modeling, and NLP algorithms, focusing on innovation, scalability, and optimization.

Technical requirements:

* English level: B2 conversational.
* Mastery of backend programming languages such as Python and JavaScript.
* Extensive knowledge of modern frameworks and tools such as Django and Flask.
* Experience with relational and non-relational databases such as MySQL, PostgreSQL, and MongoDB.
* Skills in data processing and transformation, using tools such as Apache Spark, Hadoop, or Big Data technologies.
* Knowledge in designing and implementing RESTful and/or GraphQL APIs.
* Understanding of system architectures and design patterns, including microservices.
* Solid understanding of data architectures, including schema design, optimization, and partitioning strategies.
* Design and implementation of data pipelines for efficient processing of large volumes of information.
* Advanced knowledge in Retrieval Augmented Generation (rag) systems, integrating language models such as gpt or bert with search systems.
* Development experience applying Agile principles and Scrum or Kanban Framework.
* Familiarity with development and deployment tools such as Docker, Git, Kubernetes, and cloud platforms (aws, gcp, or azure).
* Proven experience in natural language processing (NLP) projects.
* Skills in data modeling and NLP algorithms to solve complex problems.

Functions:

Data Architecture and Design:
*Design and maintain robust data architectures that support large volumes of data and facilitate the implementation of ai/nlp solutions.
* Model and optimize data structures to ensure scalability and efficiency.

Development and Maintenance of AI Backend Systems:
* Design and develop robust backend solutions that integrate nlp, rag models and large volumes of data.

Implementation of rag in nlp projects:
* integrate rag systems to optimize the generation and retrieval of information in nlp applications, such as chatbots or advanced search systems.

Optimization and Maintenance of Databases for AI:
* Manage and optimize databases to store and process training data and results of NLP models, ensuring efficiency and scalability.

API Development for AI Services:
* Create APIs and microservices to facilitate access and integration of AI data and models across multiple platforms.

Collaboration in MLOps and Data Teams:
* Work in an agile environment alongside data and MLOps teams to improve model lifecycle, deployment, and continuous iteration.