Lead AI Engineer

Responsibilities:

• Design, develop, and maintain scalable cloud-based applications using AWS services.
• Build and integrate AI/ML-powered features into applications using AWS AI services.
• Leverage AWS Kiro and AI-assisted development tools to accelerate software delivery, automate tasks, and improve code quality. Apply AI-assisted development tools to accelerate software delivery, automate repetitive tasks, and improve code quality.
• Collaborate with cross-functional teams to define, design, and ship new features.
• Implement best practices for cloud architecture, security, and performance.
• Automate deployment, monitoring, and management of cloud applications.
• Write clean, efficient, and maintainable code and contribute to both front-end and back-end development as a full-stack engineer.
• Troubleshoot and resolve issues related to cloud infrastructure and applications.
• Mentor and guide junior engineers, fostering a culture of continuous improvement.
• Stay up-to-date with the latest AWS technologies and industry trends.

Requirements:

• 7+ years of experience in software development, with a focus on cloud technologies.
• Proficiency in AWS services such as EC2 S3 Lambda, RDS, and CloudFormation.
• Strong programming skills in languages such as Python, Java, or Node.js .
• Experienced in deploying and managing applications using Red Hat OpenShift Service on AWS (ROSA), leveraging its robust platform for scalable and efficient cloud solutions.
• Experience with containerization technologies like Docker and Kubernetes.
• Familiarity with DevOps practices and tools.
• Knowledge of CI/CD pipelines and tools like Jenkins, GitLab, or AWS CodePipeline.
• Hands-on experience with AWS cloud services and building distributed systems.
• Experience integrating AI/ML capabilities into applications (e. g., personalisation, NLP, recommendations).
• Familiarity with AWS AI/ML services such as Amazon SageMaker, Bedrock, and AgentCore.
• Demonstrated experience using AI-assisted development tools (e. g., GitHub Copilot, Claude, Kiro) to accomplish tasks such as generating and reviewing code, writing and maintaining tests, creating documentation, and debugging, with the judgment to validate and own AI-generated output.
• Understanding of prompt engineering, AI workflows, or model integration patterns.
• Experience working with generative AI or LLM-based solutions is highly desirable.
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills.
• Execute with a Sense of Urgency.
• Consistently prioritises safety and security of self, others, and personal data.
• Embraces diverse people, thinking, and styles.
• Demonstrates strong ownership and accountability while fostering a collaborative, solution-oriented team culture.
• Communicates with clarity and influence, builds trust, and engages effectively with diverse stakeholders across teams and geographies.
• Continuously learns and adapts, embracing new ideas, emerging technologies, and innovative approaches to improve ways of working.
• Shows curiosity about business needs and customer priorities, aligning behaviours and decisions to deliver meaningful outcomes.
• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related technical field.
• AWS Certified Solutions Architect or Developer certification.
• Experience with microservices architecture and serverless computing.
• Understanding of networking and security principles in cloud environments.
• Familiarity with Model Context Protocol (MCP) for connecting AI models to external tools and data sources.