Sr. Software Engineer, Internal Apps
New
United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLJavascriptSnowflakeData modeling
Requirements
- 5+ years of professional software engineering experience building and shipping production systems
- Strong backend engineering skills including APIs, system design, debugging, and data modeling
- Proven experience integrating LLM APIs into real-world applications and understanding their limitations
- Hands-on experience with AI-assisted development tools such as Claude Code, Cursor, Codex, or similar
- Strong proficiency in SQL, with Snowflake experience considered a plus
- Proficiency in Python or JavaScript for backend and application development
- Ability to work independently in ambiguous environments and drive projects from idea to production
- Strong engineering judgment to distinguish between AI-suitable problems and simpler deterministic solutions
- Experience working directly with non-technical stakeholders to define and refine requirements
- Focus on building maintainable, well-structured systems with attention to performance and reliability
Responsibilities
- Partner directly with internal stakeholders to understand operational challenges and translate them into scalable software solutions
- Design, build, and maintain internal applications end-to-end, including prototyping, deployment, monitoring, and ongoing improvements
- Develop backend systems, APIs, and data models that support reliable and efficient internal workflows
- Integrate LLM APIs (e.g., OpenAI, Anthropic, Gemini) when appropriate, including prompt design and evaluation strategies
- Evaluate when to use AI-driven approaches versus deterministic systems based on performance, cost, and reliability trade-offs
- Build and maintain internal dashboards and data tools using SQL (MySQL, Snowflake) to support business decision-making
- Collaborate across engineering and business teams to identify bottlenecks and improve operational efficiency through automation
- Establish maintainable engineering patterns, including observability, security, and responsible AI usage practices
- Use AI-assisted development tools to accelerate delivery while maintaining strong code quality and review standards
- Ensure all systems are production-ready, scalable, and easy for internal teams to use and adopt
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