AI / Machine Learning Engineer

Responsibilities

• 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.