AI / Machine Learning Engineer

Design, develop, and implement Machine Learning models and AI-powered solutions. Collect, clean, preprocess, and analyze structured and unstructured datasets. Develop, train, test, and optimize Machine Learning and Deep Learning models. Apply statistical analysis and data science techniques to solve business problems. Work with Large Language Models (LLMs), Generative AI, and Natural Language Processing (NLP) where applicable. Develop and integrate AI/ML models into existing applications and business systems through APIs. Deploy, monitor, and maintain models in production environments. Evaluate model performance and continuously improve accuracy, efficiency, and scalability. Collaborate with software engineers, data engineers, and business stakeholders to deliver AI-driven solutions. Follow best practices in model versioning, documentation, testing, and deployment. Requirements Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Computer Engineering, or a related field. 1-5 years of hands-on experience in AI, Machine Learning, or related engineering roles. Strong programming skills in Python. Good knowledge of Machine Learning algorithms and techniques, including supervised and unsupervised learning. Hands-on experience with Machine Learning frameworks such as TensorFlow, PyTorch, or Scikit-learn. Strong understanding of data preprocessing, feature engineering, model training, and evaluation. Experience with Deep Learning concepts and neural networks. Good knowledge of SQL and working with structured and unstructured data. Experience in deploying and integrating ML models into production applications. Familiarity with REST APIs, Git, and software development best practices. Experience with Generative AI, Large Language Models (LLMs), and Prompt Engineering. Knowledge of Retrieval-Augmented Generation (RAG), vector databases, and embeddings. Experience with cloud AI/ML services such as AWS SageMaker, Azure Machine Learning, or Google Vertex AI. Familiarity with MLOps practices and tools such as MLflow, Docker, Kubernetes, and CI/CD pipelines. Experience with NLP, Computer Vision, or Recommendation Systems. Relevant AI/ML certifications are a plus.