AI Engineer (Night Shift)

Key Responsibilities:

• Design, develop, and implement AI models to address real-world problems in areas such as NLP, computer vision, robotics, or autonomous systems.
• Build and optimize AI-driven solutions across various applications, ensuring they are scalable, efficient, and high-performing.
• Develop custom algorithms, architectures, and frameworks for deep learning and reinforcement learning systems.
• Preprocess and analyze data to extract meaningful insights and prepare datasets for AI model development.
• Implement cutting-edge natural language processing (NLP) , computer vision , and speech recognition models using state-of-the-art techniques (e.g., Transformers, CNNs, RNNs).
• Collaborate with data scientists , machine learning engineers , and software developers to integrate AI solutions into production systems.
• Optimize AI algorithms for speed , accuracy , and scalability in real-time environments.
• Work on AI model deployment using frameworks like TensorFlow Serving , PyTorch , and containerization tools such as Docker and Kubernetes .
• Monitor, maintain, and continuously improve deployed AI models based on real-world feedback and performance metrics.
• Stay up-to-date with the latest research and advancements in AI and machine learning to ensure the company leverages the most innovative technologies.
• Conduct rigorous testing, debugging, and validation to ensure robustness and reliability of AI models.
• Document processes, algorithms, and model architectures for future reference and cross-team collaboration.

• Bachelor’s, Master’s, or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, or a related field.
• Proven experience (2-5 years) in designing and implementing AI models using popular frameworks such as TensorFlow , PyTorch , Keras , or MXNet .
• Strong proficiency in Python , with experience in AI-related libraries (e.g., NumPy , Pandas , OpenCV , Scikit-learn ).
• Experience with deep learning models, especially for tasks such as NLP , computer vision , and recommendation systems .
• Strong understanding of machine learning algorithms , including supervised and unsupervised learning, reinforcement learning , and transfer learning .
• Familiarity with cloud-based AI services (e.g., AWS SageMaker, Google AI Platform, Azure ML).
• Experience with data processing pipelines and tools such as Apache Kafka , Apache Spark , or TensorFlow Data .
• Proficiency in using version control systems like Git .
• Experience deploying AI models into production environments and optimizing them for performance and scalability.
• Knowledge of data engineering principles, including handling large datasets, databases, and data pipelines.
• Familiarity with containerization technologies (Docker, Kubernetes) and cloud infrastructure.
• Strong problem-solving and analytical skills, with the ability to work independently and collaborate in a team setting.
• Excellent written and verbal communication skills.