AWS Cognitive Engineer (Cloud & AI Developer)

Role Overview
Our client, a leading technology-driven organization, is seeking an experienced AWS Cognitive Engineer to design and deliver cloud-native applications enhanced with artificial intelligence capabilities.

In this role, you will be responsible for developing scalable, secure, and intelligent solutions on AWS, working closely with cross-functional teams to translate business needs into technical implementations. You will also play a key role in optimizing application performance, enhancing AI models, and ensuring adherence to best practices in cloud and responsible AI.



Key Responsibilities

• Act as a subject matter expert in AWS cloud services and AI/ML technologies
  
• Partner with cross-functional teams to define architecture and drive key technical decisions
  
• Design and implement AI-powered features using AWS AI/ML services
  
• Build and deploy applications using serverless and microservices-based architectures
  
• Promote best practices and support knowledge sharing within the team
  
• Monitor application and model performance, and implement improvements for scalability and accuracy
  
• Ensure solutions align with security, governance, and responsible AI standards
  
• Contribute to project planning, timelines, and successful delivery of milestones
  

Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field
  
• Minimum of 3 years of hands-on experience working with AWS services
  
• At least 1 year of experience in AI/ML projects or exposure to AWS AI services is preferred
  
• Relevant AWS certifications (e.g., Solutions Architect or Machine Learning Specialty) are an advantage
  
• Strong analytical thinking and problem-solving skills
  
• Effective communication and collaboration abilities
  
• Ability to lead technical discussions and support team development
  

Technical Skills

• Strong expertise in core AWS services such as compute, storage, serverless, databases, and identity management
  
• Solid understanding of cloud architecture and distributed systems
  
• Experience working with AWS AI/ML services (e.g., model development, natural language processing, image recognition)
  
• Familiarity with generative AI concepts and prompt engineering techniques
  
• Knowledge of MLOps practices, including model deployment and monitoring
  
• Experience with serverless frameworks and container technologies (e.g., Docker, Kubernetes)
  
• Proficiency in Python or similar programming languages for AI and backend development
  
• Understanding of security best practices in cloud and AI environments