Senior AI Engineer

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.