Ai Engineer

hace 2 meses

Lima, Perú GSB A tiempo completo

**Job Summary**:
**Key Responsibilities**:

- Design, develop, and deploy hybrid RAG architectures integrating LLMs with retrieval-based systems for improved relevance and contextual responses.
- Fine-tune and optimize large language models, enhancing their performance and adaptability to domain-specific requirements.
- Implement and manage RAG pipelines that effectively combine retrieval mechanisms with generative capabilities, ensuring high accuracy and efficiency.
- Develop custom plugins, adapters, or APIs to integrate retrieval systems (e.g., Elasticsearch, FAISS) with generative models, facilitating seamless information retrieval.
- Monitor and troubleshoot issues within RAG pipelines, fine-tuning retrieval parameters and model hyperparameters to optimize performance.
- Work closely with data engineers to manage and preprocess large datasets for training, ensuring high-quality and diverse data coverage.
- Evaluate and benchmark the performance of RAG solutions, using metrics such as response accuracy, latency, and user satisfaction.
- Stay up-to-date with advancements in NLP, LLMs, and RAG methodologies, continually improving existing architectures and recommending new techniques.

**Qualifications**:

- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field, or equivalent practical experience.
- 3+ years of experience in AI/NLP, with a focus on LLMs, transformer-based architectures, and retrieval systems.
- Proven experience building and deploying RAG solutions or other hybrid AI architectures.
- Strong understanding of information retrieval methods, including dense retrieval, sparse retrieval, and embeddings-based techniques.
- Proficiency in Python, TensorFlow or PyTorch, and experience with libraries and tools related to LLMs, such as Hugging Face Transformers.
- Familiarity with retrieval frameworks like Elasticsearch, FAISS, or OpenSearch.
- Knowledge of prompt engineering, fine-tuning, and deployment of language models for production environments.
- Strong analytical skills, with experience in optimizing LLM and retrieval model performance.
- **English Advanced Mandatiry**

**Preferred Skills**:

- Experience with cloud services and infrastructure (AWS, GCP, Azure) and MLOps tools for model deployment and monitoring.
- Contributions to open-source RAG projects or experience working with OpenAI, LangChain, or similar frameworks.
- Knowledge of vector databases, memory-augmented networks, and distributed systems.

**Education**:

- Bachelor's (preferred)

**Experience**:

- English: 5 years (preferred)

**Language**:

- English (required)

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