Artificial Intelligence Developer - Large Language Models

About the Role :

We are looking for a passionate and skilled AI/ML Engineer with strong experience in Python, TensorFlow, PyTorch, and Neural Networks, particularly in the area of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). As part of our AI Innovation team, you will play a critical role in building and deploying advanced AI solutions that power intelligent applications and platforms.

Key Responsibilities :

- Implement Retrieval-Augmented Generation (RAG) pipelines to enhance LLM capabilities with context-aware and real-time data retrieval.

- Build, train, and optimize deep learning models using TensorFlow and PyTorch for a variety of NLP and generative tasks.

- Develop robust, scalable, and production-ready AI solutions that can integrate into existing software ecosystems.

- Conduct experiments, A/B testing, and performance evaluations on model variants.

- Collaborate with data scientists, product managers, and software engineers to translate business

requirements into AI/ML use cases.

- Stay up to date with the latest research and developments in the field of generative AI and machine learning.

- Document technical specifications, model performance metrics, and deployment processes.

Required Skills & Experience :

- Hands-on experience in TensorFlow and PyTorch for developing and deploying neural network models.

- Practical understanding of Neural Networks architectures such as CNNs, RNNs, Transformers, Attention mechanisms, etc.

- Experience working with Large Language Models (LLMs) open-source or commercial (e.g., OpenAI, HuggingFace, Cohere, etc.).

- Proven track record implementing RAG pipelines using vector databases (e.g., FAISS, Pinecone, Weaviate) and embedding models.

- Experience working with cloud platforms (e.g., AWS, Azure, GCP) for model training and deployment.

- Understanding of ML Ops principles, including version control, reproducibility, and monitoring.

- Ability to write clean, modular, and well-documented code.