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