Generative AI Engineer

1. Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or a related field.
2. Proven hands-on experience in building and deploying Gen AI and Deep Learning models.
3. Strong proficiency in Python and its core data science libraries (e.g., TensorFlow, PyTorch, Hugging Face, LangChain).
4. In-depth knowledge of LLM architecture, LLM training methodologies, and fine-tuning techniques (e.g., LoRA, QLoRA).
5. Solid understanding of Neural Network fundamentals.
6. Experience with NLP, including text processing and embedding preparation (e.g., Word2Vec, GloVe, sentence transformers).
7. Practical experience implementing RAG pipelines and vector databases (e.g., Pinecone, ChromaDB).
8. Familiarity with APIs from major AI providers like OpenAI, Google.
9. Strong problem-solving skills and the ability to work independently and in a team environment.

Preferred Qualifications

1. Experience with MLOps practices and tools (e.g., Docker, Kubernetes, MLflow).
2. Familiarity with cloud computing platforms (e.g., AWS, GCP, Azure).
3. Contributions to open-source AI/ML projects or a portfolio of relevant work.
4. Published research in the field of AI, NLP, or Machine Learning.

Job responsibilities

Key Responsibilities:

1. Design, develop, and implement advanced Generative AI solutions using state-of-the-art models and techniques.
2. Train, fine-tune, and evaluate Large Language Models (LLMs) for specific use cases, ensuring optimal performance and accuracy.
3. Develop and integrate Retrieval-Augmented Generation (RAG) systems to enhance model responses with external knowledge bases.
4. Apply expertise in Deep Learning and Neural Networks to build robust and scalable AI systems.
5. Utilize Natural Language Processing (NLP) techniques for tasks such as text classification, sentiment analysis, and named entity recognition.
6. Manage the full lifecycle of AI models, including embedding preparation, data preprocessing, model deployment, and monitoring.
7. Write clean, efficient, and well-documented code, primarily in Python.
8. Collaborate with cross-functional teams, including product managers, data scientists, and software engineers, to deliver high-impact AI features.
   1. Stay current with the latest advancements in the field, including new models from OpenAI and other research institutions.