Software Engineer

A Day in the Life

• Design, build, test, and ship scalable, efficient, and reusable frontend and backend applications.
• Own features end-to-end—from understanding the problem and design through development, testing, deployment, and production.
• Apply AI-assisted software development throughout the engineering lifecycle, using AI coding tools to accelerate development, improve code quality, generate tests, debug issues, and explore solutions.
• Embrace the AI Development Life Cycle (AIDLC) and continuously evolve how software is designed, developed, tested, deployed, and operated in an AI-native engineering environment.
• Think beyond individual components and understand how your work impacts the broader system, platform, and customer experience.
• Identify and drive improvements in performance, scalability, reliability, security, and engineering quality.
• Collaborate with Product, Design, AI, and Engineering teams to turn ambiguous problems into simple, scalable solutions.
• Proactively identify technical challenges, challenge assumptions, and drive solutions without waiting for direction.
• Contribute to engineering excellence through design reviews, code reviews, testing, documentation, and mentoring.

What You Need

• 3+ years of software engineering experience, with a strong ownership mindset and willingness to take on challenging problems.
• Python: strong, hands-on experience building production-grade services.
• Good understanding of REST APIs, RDBMS, NoSQL databases, distributed systems, and cloud-native architectures.
• Experience building and integrating end-to-end applications.
• Hands-on experience using AI coding tools as a coding partner, not simply as a chatbot.
• Understanding of AIDLC and AI-native software development practices, including AI-assisted coding, test generation, debugging, code review, documentation, and iterative development.
• Strong problem-solving skills, with the ability to diagnose complex problems and develop pragmatic solutions.
• A systems-thinking mindset: you understand not just how something works, but why it works and how it affects the larger system.
• Ability to ramp up quickly, work independently, and deliver with minimal hand-holding.
• A bias for action, curiosity, and continuous learning, with the judgment to know when to move fast and when to go deep.

Good to Have

• Hands-on experience building Agentic AI applications or workflows.
• Experience with LLMs, RAG, tool calling, MCP, AI agents, agent orchestration, or multi-agent systems.
• Experience integrating AI capabilities into production applications.
• Familiarity with evaluating, monitoring, and improving AI-powered systems.
• Experience with cloud platforms such as AWS, Azure, or GCP.