Role Overview :

We are seeking a forward-looking Software Development Engineer to design, build, and scale next-generation applications leveraging cloud-native architectures, AI/ML capabilities, and modern engineering practices.

The ideal candidate is a strong software engineer with expertise in distributed systems, AWS cloud technologies, platform engineering, and AI-powered application development. This individual will help establish foundational capabilities for AI agents, enable scalable agent orchestration frameworks, and drive adoption of AI-assisted software development practices across the engineering organization.

The role requires a passion for innovation and emerging AI technologies. The successful candidate will play a key role in building reusable agentic capabilities, integrating Large Language Models (LLMs), enabling AI-driven workflows, and delivering foundational services that accelerate the development of intelligent applications across the enterprise.

Key Responsibilities :

- Design, develop, and maintain scalable cloud-based applications using AWS services.

- Build and integrate AI/ML-powered features and agents into applications using AWS AI services.

- Leverage AWS Kiro and AI-assisted development tools to accelerate software delivery, automate 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.

Minimum Qualifications:

- 3+ 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).

- 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., personalization, 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.

- 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 prioritizes safety and security of self, others, and personal data.

- Embraces diverse people, thinking, and styles.

Behavioural Competencies:

- 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 behaviors and decisions to deliver meaningful outcomes.

Preferred Qualifications:

- Bachelors 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.