AI Engineer

Overview

We are seeking a highly skilled AI Engineer to design and implement intelligent systems that drive automation, insights, and innovation. You will work on machine learning (ML), deep learning and optimization algorithms collaborating with cross-functional teams building scalable AI solutions for real-world applications.

Key Responsibilities

• Design, develop and optimize ML/AI models for classification, regression, and recommendation systems.
• Implement machine learning pipelines (end-to-end), from data preprocessing to model deployment.
• Develop and fine-tune deep learning architectures using TensorFlow, PyTorch etc.
• Apply MLOps best practices for model versioning, monitoring, and retraining.
• Work with large-scale datasets, performing feature engineering and data augmentation.
• Optimize model performance for scalability, latency, and efficiency in production.
• Deploy AI models using MLOps practices, containerization (Docker), and cloud services (Azure).
• Research and experiment with state-of-the-art AI techniques to improve existing models and integrate cutting-edge techniques into production systems.
• Collaborate with data scientists and product teams to integrate AI into business applications.

Qualifications & Skills

Required:

• Bachelor's/Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
• Strong programming skills in Python
• Experience in training and deploying ML models in production.
• Proficiency with AI/ML frameworks (TensorFlow, PyTorch, Hugging Face, Scikit-learn).
• Knowledge of cloud computing (AWS, GCP, Azure) and MLOps tools.
• Understanding of data structures, algorithms, and optimization techniques.
• Experience with data engineering, feature extraction, and model evaluation.
• Knowledge of deep learning techniques (CNNs, RNNs, Transformers, GANs, etc.).
• Experience with LLMs (GPT, BERT, LLaMA, T5) and fine-tuning models.
• Knowledge of multi-modal AI (text, image).

Nice to Have:

• Knowledge of reinforcement learning, planning, and decision-making models.
• Hands-on experience with big data processing and big data frameworks
• Familiarity with vector databases.
• Experience in real-time AI applications for high-performance systems.