Sr. Software Engineer

Required Skills & Experience

3-5+ years of full stack (50/50) development experience Proficiency in one or more modern programming languages (e.g., Python, JavaScript/TypeScript, Java (backend), Go, C#). Experience designing, developing, and supporting scalable applications across front-end, back-end, APIs, databases, and cloud infrastructure. Experience building or supporting solutions utilizing Retrieval-Augmented Generation (RAG), machine learning pipelines, Large Language Models (LLMs), or other AI-driven technologies. Experience validating AI-generated code, troubleshooting complex technical issues, and delivering production-ready software solutions.

Nice to Have Skills & Experience

Bachelor's degree in Computer Science, Engineering, or equivalent practical experience. Experience with cloud platforms, preferably AWS. Ability to clearly explain technical design decisions, evaluate tradeoffs, and communicate complex concepts to technical and non-technical stakeholders. Experience building scalable distributed systems and cloud-native applications.

Job Description

Design, code, test, and deploy robust software solutions with a focus on reliability, scalability, and maintainability. Take ownership of moderately complex features, services, and technical initiatives. Design, develop, and support full-stack applications across front-end, back-end, APIs, databases, and cloud environments. Work closely with other engineers, product managers, and designers to build features that solve real customer problems. Contribute to and uphold high standards of code quality through code reviews, unit testing, and documentation. Analyze and resolve technical issues across the software stack. Troubleshoot production issues and implement sustainable fixes. Leverage AI-assisted development tools to improve development efficiency while applying sound engineering judgment and validating generated solutions. Contribute to the design and implementation of solutions that may incorporate machine learning capabilities, Retrieval-Augmented Generation (RAG) architectures, and Large Language Model (LLM) integrations.