• Talk to people, then build things. You'll work directly with business and engineering teams to understand what's slowing them down. You'll figure out whether the fix is traditional software, an AI integration, or some combination. Then you'll turn vague requirements into something you can actually build. • Own the whole thing. Prototype it, harden it, deploy it, monitor it. These are internal tools, so you move fast, but they still need to work reliably. • Build fast with AI-assisted development. We use tools like Claude Code to move quickly. You should be comfortable using them to scaffold, iterate, and ship. But you also need to read what they produce, catch what they get wrong, and know when to write it yourself. These tools make good engineers faster. They don't replace knowing how software works. • Use AI where it actually helps. Integrate LLM APIs (OpenAI, Anthropic, Gemini, etc.) when language understanding or probabilistic reasoning genuinely improves the outcome. Design prompt strategies and evaluation methods when you do. Skip the model call when a conditional statement would work better. Keep an eye on cost, latency, and reliability. • Write code other people can maintain. Build clean systems. Establish practical patterns for secure AI usage. Contribute to standards around observability, safety, and data handling. • Work with data. Write SQL against MySQL/ Snowflake, build internal dashboards, and turn business questions into lightweight data tools.

• Design and ship production AI features end-to-end — RAG, agents, document understanding, conversational interfaces — across LangGraph / LiteLLM / pgvector / Langfuse. • Drive technical architecture for the AI product line: LLM orchestration, evals, observability, latency / cost / reliability tradeoffs. • Own AI initiatives technically — from spec through production, including rollout and post-launch eval improvements. • Code review and architectural feedback across the team; mentor engineers on AI engineering practices. • Build and evolve eval frameworks and quality measurement for AI features. • Collaborate closely with PM, Design, and adjacent teams to shape AI features that work for real users at scale. • Bring proven patterns from the LLM ecosystem (frameworks, agent patterns, MCP, tool use) into the team.

• Orchestrate complex multi-LLM workflows and define best practices for AI-native coding and code verification. • Develop broad solutions that add value across the entire ecosystem while aggressively managing technical debt. • Mentor Senior and II-level engineers, fostering a culture of trust, respect, and high performance. • Collaborate with stakeholders to shape product roadmaps and refine the SDD framework for maximum efficiency. • Ensure all AI-generated solutions adhere to strict security, performance, and maintainability standards at scale.

• Design and build full-stack features that address enterprise adoption requirements across Observability • Own integrations and infrastructure capabilities end-to-end—from API design and backend services to UI surfaces • Collaborate with product managers, designers, solutions engineers, and customers to understand enterprise requirements and deliver against them • Participate in architecture and design discussions, making well-reasoned trade-offs for scalability, reliability, and maintainability • Write well-tested, observable code and uphold high engineering standards across the team • Engage with internal stakeholders and occasionally enterprise customers to validate solutions and gather feedback • Participate in on-call rotations and maintain production reliability for your features