AI Engineer

About the Role We are seeking a talented AI Engineer with 2-3 years of professional experience to join our growing team. As a AI Engineer, you will collaborate with data scientists, software engineers, and product teams to design, build, and deploy ML models that drive value across our organisation. This is an excellent opportunity for someone early in their ML career who wants to gain hands-on experience with real-world applications while working alongside experienced professionals. Responsibilities Implement and optimize ML algorithms and models based on requirements from data scientists and product teams Work with Large Language Models (LLMs) to develop AI-powered applications and features Design prompts, fine-tune, and evaluate LLM performance for specific use cases Build systems to integrate LLMs with existing products and services Design and develop ML/DL pipelines for data preprocessing, model training, evaluation, and deployment Experience in using RAG Collaborate with software engineers to integrate ML/DL models into production systems Monitor and maintain deployed ML/DL models, ensuring reliability and performance Participate in code reviews and contribute to engineering best practices Stay current with the latest ML/DL research and technologies, evaluating their potential application to business problems Assist in collecting and preparing datasets for model training and validation Document model architecture, data flows, and deployment procedures Strong in Machine Learning and Deep learning Basics Requirements Bachelor's degree in Computer Science, Statistics, Applied Mathematics, or a related field (Master's preferred but not required) 1-2 years of professional experience building and deploying machine learning models Strong programming skills in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or scikit-learn Experience with Large Language Models (LLMs), including understanding of transformer architectures, fine-tuning approaches, and prompt engineering Knowledge of NLP concepts and techniques applicable to LLMs (embeddings, tokens, context windows, etc.) Experience with data processing libraries like Pandas, NumPy, and visualization tools like Matplotlib or Seaborn Understanding of ML fundamentals, including supervised/unsupervised learning, feature engineering, and model evaluation Knowledge of software engineering best practices (version control, testing, CI/CD) Basic understanding of cloud platforms (AWS, GCP, or Azure) and containerization (Docker)