AI Engineer - GJT

We are seeking an AI Engineer with extensive experience in AI/ML development and deployment to join our team.
Key Responsibilities:

• AI Model Development & Implementation: Design and develop AI models and machine learning algorithms for various business needs.
  
• Deployment: Deploy AI models into production environments using cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
  
• MLOps: Develop and maintain machine learning pipelines following MLOps best practices to ensure smooth deployment and scaling of models.
  
• Optimization: Continuously optimize models for better performance (efficiency, accuracy, scalability).
  
• Collaboration: Work closely with data scientists, software engineers, and domain experts to effectively integrate AI models into applications.
  
• Continuous Learning: Stay up-to-date with the latest AI advancements and techniques to ensure cutting-edge solutions.
  

Required Skills & Qualifications:

• Experience: Minimum of 5+ years working in AI/ML development and deployment.
  
• Programming Skills: Proficient in Python and familiar with ML frameworks like TensorFlow, PyTorch, Keras, or Scikit-learn.
  
• Cloud-Based AI Services: Experience working with cloud platforms and AI services such as AWS SageMaker, Google Vertex AI, or Azure ML.
  
• Deep Learning: Knowledge of deep learning architectures (CNNs, RNNs, Transformers) and NLP (Natural Language Processing) techniques.
  
• Data Processing: Skilled in data processing and storage solutions (SQL, NoSQL, Spark, Hadoop).
  
• MLOps & Deployment: Strong understanding of MLOps concepts, model versioning, and deployment practices.
  
• API Integration: Experience with APIs and integrating AI models into applications.
  

Preferred Qualifications:

• CI/CD: Experience with continuous integration/continuous deployment (CI/CD) pipelines for machine learning models.
  
• Edge AI: Familiarity with deploying AI models on mobile/IoT devices and edge computing environments.
  
• Specialized AI Techniques: Experience with NLP, reinforcement learning, generative AI, or Large Language Models (LLMs).
  
• Federated Learning: Knowledge of federated learning techniques for privacy-preserving machine learning.