AI Engineer - Data Science & Generative AI

About the opportunity

Join Microsoft as an AI Engineer building intelligent data-driven applications using machine learning, generative AI, large language models, and modern cloud technologies.

What you will do

• check_circle Design, train, evaluate, and optimize machine learning and deep learning models for real-world business applications.
• check_circle Develop generative AI and LLM-powered applications using prompt engineering, embeddings, vector databases, and retrieval-augmented generation techniques.
• check_circle Analyze large structured and unstructured datasets using Python, SQL, Pandas, NumPy, and statistical modeling techniques.
• check_circle Build and deploy scalable AI services and APIs using cloud platforms, containerization, and modern MLOps practices.
• check_circle Collaborate with data scientists, software engineers, and product teams to translate business requirements into production-ready AI solutions.
• check_circle Monitor model performance, perform experimentation and validation, and improve accuracy, reliability, scalability, and responsible AI compliance.

What we are looking for

• arrow_circle_right Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics, Statistics, or a related technical field.
• arrow_circle_right Hands-on proficiency with Python, SQL, Pandas, NumPy, and machine learning libraries such as Scikit-learn, TensorFlow, or PyTorch.
• arrow_circle_right Strong understanding of statistics, supervised and unsupervised machine learning, feature engineering, model evaluation, and data preprocessing.
• arrow_circle_right Practical knowledge of generative AI, large language models, prompt engineering, embeddings, RAG, or transformer-based architectures.
• arrow_circle_right Experience building data science or AI projects and communicating technical results to engineering, product, or business stakeholders.
• arrow_circle_right Familiarity with Azure, AWS, or GCP and basic knowledge of model deployment, APIs, Git, Docker, or MLOps workflows.

Skills & Tech Stack