Senior AI Builder

What You'll Do

• Design agents that take real actions: prompts, reasoning chains, tool calls, full architecture including MCP servers, agent scaffolding, context harnesses, and a Skills Marketplace. Build production infrastructure that every squad at EarnIn builds on.
• Rethink the product development lifecycle (PDLC): replace manual friction with agentic workflows in scoping, design, review, testing, deployment, and monitoring. Build evaluation pipelines, automated PR hygiene, deployment gating, and generation-to-merge metrics.
• Own evaluation infrastructure: build pipelines, benchmarks, and quality gates for AI-assisted workflows. Design eval harnesses, set generation-to-merge and review latency baselines, make model quality visible and trustworthy.
• Turn AI pilots into production: build reusable libraries, templates, and reference implementations that squads can fork and ship quickly. Focus on reusable, reliable AI integration patterns.

What Success Looks Like

• Teams across engineering ship AI-assisted features faster with fewer rework loops.
• Harnesses built are actively used and well-documented.
• AI pilot quality and safety metrics are visible, trustworthy, and improving.

Requirements

• 4+ years of full-time software engineering experience, with at least 2 years building tooling, platforms, or internal developer products.
• Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, or related technical discipline, or equivalent industry experience.
• Hands-on experience with LLM integration patterns: prompt engineering, RAG pipelines, tool/function calling, and agent architectures.
• Proficiency working across the stack when needed.
• Experience with MCP, LangChain, or comparable orchestration frameworks.
• Experience with open source LLM models.
• Strong opinions about developer experience and track record of building things other engineers actually use.

Nice-to-Haves

• Hands-on experience with reinforcement learning (RLHF, RLAIF, or reward modeling) in applied product contexts.
• Experience in fintech or regulated/security-sensitive environments.
• Hands-on work with AI governance: bias evaluation, audit logging, model cards.
• Exposure to multi-step reasoning pipelines or human-in-the-loop system design.