Description

As an Application Developer specializing in AWS and AI, you will design, build, and configure cloud-native applications that integrate advanced AI capabilities to meet business objectives. Your role involves collaborating across teams to deliver scalable, secure, and intelligent solutions. You will troubleshoot issues, implement enhancements, and ensure optimal performance of applications leveraging AWS and AI services.

• Serve as a Subject Matter Expert (SME) in AWS and AI technologies.
• Collaborate with cross-functional teams and lead key technical decisions.
• Design and implement AI-driven features using AWS AI/ML services (e.g., SageMaker, Bedrock, Rekognition).
• Develop and deploy applications using serverless architecture and microservices on AWS.
• Facilitate knowledge sharing and best practices for cloud and AI development.
• Monitor application and AI model performance; implement improvements for scalability and accuracy.
• Ensure compliance with security, governance, and responsible AI standards.
• Assist in project planning, timelines, and adherence to delivery milestones.

Requirements

• Education: Bachelor’s degree in Computer Science, Information Technology, Engineering, or related field.
• Experience: Minimum 3 years of hands-on experience with AWS services.
• At least 1 year of experience in AI/ML projects or AWS AI services preferred.
• Certifications: AWS Certified Solutions Architect or AWS Machine Learning Specialty is a plus.
• Soft Skills: Strong problem-solving and analytical skills.
• Excellent communication and collaboration abilities.
• Ability to lead technical discussions and mentor team members.
• Professional & Technical Skills: Expert proficiency in Amazon Web Services (AWS) including EC2, S3, Lambda, RDS, and IAM.
• Strong understanding of cloud computing concepts and architecture.
• Experience with AWS AI/ML services (SageMaker, Bedrock, Comprehend, Rekognition).
• Familiarity with generative AI concepts and prompt engineering.
• Knowledge of MLOps practices for deploying and monitoring machine learning models.
• Experience with serverless architecture and containerization (Docker, Kubernetes).
• Proficiency in Python or other programming languages for AI integration.
• Knowledge of security best practices in cloud and AI environments.