Hybrid AI Solutions Developer

hace 3 días

Bogotá, Bogotá D.E., Colombia GSB Solutions A tiempo completo

GSB Solutions is seeking an experienced Hybrid AI Solutions Developer to join our team in building and optimizing cutting-edge AI solutions leveraging Large Language Models (LLMs). As a key member of our team, you will design, develop, and deploy hybrid AI architectures integrating LLMs with retrieval-based systems for improved relevance and contextual responses.

We are looking for a skilled professional with 3+ years of experience in AI/NLP, specifically in LLMs, transformer-based architectures, and retrieval systems. You should have a strong understanding of information retrieval methods, including dense retrieval, sparse retrieval, and embeddings-based techniques.

The ideal candidate will have hands-on experience with Python, TensorFlow or PyTorch, and libraries related to LLMs, such as Hugging Face Transformers. Familiarity with retrieval frameworks like Elasticsearch, FAISS, or OpenSearch is also required.

In this role, you will work closely with cross-functional teams to develop scalable solutions that enhance information retrieval, comprehension, and generation capabilities in real-world applications. You will be responsible for fine-tuning and optimizing large language models, implementing and managing RAG pipelines, and developing custom plugins, adapters, or APIs to integrate retrieval systems with generative models.

A successful candidate will have a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field, or equivalent practical experience. They should stay up-to-date with advancements in NLP, LLMs, and RAG methodologies, continually improving existing architectures and recommending new techniques.

Key Responsibilities:

  • Design, develop, and deploy hybrid RAG architectures integrating LLMs with retrieval-based systems.
  • Fine-tune and optimize large language models for improved performance and adaptability.
  • Implement and manage RAG pipelines combining retrieval mechanisms with generative capabilities.
  • Develop custom plugins, adapters, or APIs to integrate retrieval systems with generative models.
  • Monitor and troubleshoot issues within RAG pipelines, fine-tuning retrieval parameters and model hyperparameters.

Requirements:

  • 3+ years of experience in AI/NLP, specifically in LLMs, transformer-based architectures, and retrieval systems.
  • Strong understanding of information retrieval methods, including dense retrieval, sparse retrieval, and embeddings-based techniques.
  • Hands-on experience with Python, TensorFlow or PyTorch, and libraries related to LLMs, such as Hugging Face Transformers.
  • Familiarity with retrieval frameworks like Elasticsearch, FAISS, or OpenSearch.

Salary:$120,000 - $180,000 per year, depending on experience.

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