Software Development Engineer I

• Code Mastery: Own feature development and write clean, efficient, maintainable, and production-quality code that drives the product forward.

• Code Mastery: Own feature development and write clean, efficient, maintainable, and production-quality code that drives the product forward.
• Technical Contribution: Participate actively in architecture and design discussions and contribute to building scalable, secure, and high-performance solutions.
• Code Review: Conduct thorough code reviews to maintain code quality, identify opportunities for improvement, and provide constructive feedback to other engineers.
• Cloud Integration: Work with cloud services such as AWS Lambda, API Gateway, EC2, S3, RDS, and other relevant AWS services to build and optimize cloud-based solutions.
• Problem Solving: Troubleshoot and resolve complex technical problems by identifying root causes and implementing effective, scalable solutions.
• Collaboration: Work closely with product managers, designers, engineers, and other stakeholders to ensure technical solutions are aligned with product and business objectives.
• Project Execution: Contribute to project planning and execution and take ownership of delivering features within agreed timelines and scope.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 1–3 years of professional software development experience building scalable backend systems.
• Strong proficiency in Go, Python, or Node.js for backend development.
• Experience designing and building RESTful APIs and microservices architectures.
• Solid understanding of distributed systems concepts such as concurrency, caching, asynchronous processing, and fault tolerance.
• Hands-on experience with cloud platforms such as AWS, GCP, or Azure.
• Experience building and deploying applications in containerized environments using Docker and Kubernetes.
• Strong understanding of SQL and NoSQL databases, including performance optimization techniques.
• Experience participating in system design discussions and architecture decisions.
• Ability to work effectively in a fast-paced environment with a high degree of ownership.
• Strong problem-solving and debugging skills.
• Working knowledge of Generative AI and LLM fundamentals, including how LLMs work, tokens, context windows, prompting, hallucinations, and common limitations.