AI Engineer

About the job

An AI Engineer designs, builds, tests, and deploys Artificial Intelligence and Machine Learning systems that solve real-world business problems. The role bridges data science and software engineering, ensuring AI models are production-ready, scalable, and secure. Key Responsibilities • Design and develop AI/ML models (classification, prediction, NLP, vision, recommendation systems). • Build data pipelines for training, validation, and inference. • Train, fine-tune, and evaluate models using frameworks like TensorFlow, PyTorch, or Scikit-learn. • Integrate AI models into applications via APIs and microservices. • Optimize models for performance, accuracy, and scalability. • Deploy models on cloud platforms (AWS, Azure, GCP) or on-prem systems. • Monitor model performance and retrain when data drifts. • Ensure data security, privacy, and ethical AI compliance. • Collaborate with product managers, data scientists, and software engineers. Required Skills • Strong programming in Python (and/or Java, C++). • Solid foundation in Machine Learning, Deep Learning, and Statistics. • Experience with TensorFlow, PyTorch, Keras, Scikit-learn. • Knowledge of NLP, Computer Vision, or Generative AI (LLMs). • Data handling using Pandas, NumPy, SQL. • Model deployment using Docker, REST APIs, CI/CD. • Cloud and MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI). Qualifications • Bachelor’s/Master’s in Computer Science, AI, Data Science, or related field. • 2–5+ years of experience in ML/AI development (for mid-level roles). • Strong problem-solving and analytical skills.

Requirements

• AI/ML models
• Data pipelines
• TensorFlow
• PyTorch
• Scikit-learn
• APIs
• Cloud deployment

Qualifications

• Bachelor’s/Master’s in Computer Science
• AI
• Data Science
• related field

Preferred Technologies

• AI/ML models
• Data pipelines
• TensorFlow
• PyTorch
• Scikit-learn
• APIs
• Cloud deployment