About the role
You'll lead day-to-day engineering and write code alongside a small, cross-functional team. Your focus will be the backend, cloud infrastructure, and reusable patterns that help engineers and domain experts build reliable products with AI. You'll work ahead of product needs, make pragmatic architecture decisions, and help the team move from ideas to production quickly while protecting quality.
What you'll do
- Lead engineering delivery, contribute code daily, and give the team clear technical direction.
- Own backend architecture, APIs, database schemas, agent orchestration, and cloud infrastructure across Google Cloud and AWS.
- Build reusable foundations and documented patterns that keep product builders unblocked as new features take shape.
- Make AI agents part of testing, project coordination, and code review, with clear verification and human accountability.
- Establish secure, scalable environments, with appropriate isolation and reliable paths from preview to production.
- Enforce type checks, linting, automated tests, and DeepSource security and quality checks. Require domain-expert review before changes are merged.
- Partner with engineering and product leadership on priorities, short iteration cycles, tradeoffs, and technical risk.
- Mentor engineers, help hire and shape the team, and turn recurring problems into lasting improvements to tools and standards.
What you bring
- 7+ years of software engineering experience, including senior ownership of production systems at scale.
- 2+ years leading engineers, as a formal manager or technical lead, with experience developing people and shaping a team.
- Strong backend and infrastructure skills, including Python, FastAPI, and Google Cloud or AWS.
- Full-stack fluency, including React and TypeScript, so you can support work across the product when needed.
- A track record of making practical architectural decisions for enterprise systems, balancing immediate delivery with long-term reliability.
- Experience improving engineering delivery, testing, security, and operational quality.
- Clear communication with technical and nontechnical teammates, including explaining decisions and their tradeoffs.
Nice to have
- Experience with AI-assisted development tools such as Cursor or Claude Code, including choosing models and checking generated work.
- Experience with workflow orchestration, AI agents, or products that use large language models.
- Experience in a startup or small team where priorities evolve and people work across disciplines.
How we work
AI is part of how we work every day. We use it to accelerate analysis, development, and review, with people accountable for the quality of the result. We communicate clearly, document decisions, share feedback early, and follow work through to completion.
For engineering work, we plan meaningful changes before coding, favor simple designs, and build review lessons into reusable checks. Engineers own architecture, verification, and production readiness.
What success looks like
- Product builders can deliver features without recurring backend or infrastructure bottlenecks.
- AI supports the team's daily development, testing, and review work, with clear ownership of the results.
- Delivery and review become faster, with fewer defects reaching production.
- Engineers make decisions with confidence, and the product remains stable as the team ships more frequently.
About Enlighten
Enlighten AI Labs builds enterprise AI products and analytics solutions that help organizations turn complex data into clear decisions. Our products include Aria (an AI analyst that turns business data into insights and recommendations) and Echo (an analytics quality assurance platform that checks whether digital tracking matches measurement requirements). We work with enterprise teams across industries, combining analytics expertise, AI, and hands-on delivery to solve business problems.