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 Originally posted on Himalayas