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