Senior AI Builder
Senior AI Builder responsible for designing and shipping production AI agents, agentic workflows, evaluation harnesses, and reusable AI tooling that transform EarnIn's product development lifecycle. Requires 4+ years software engineering experience with strong LLM/agent expertise.
About the job
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.
Skills
Llm Integration, Prompt Engineering, RAG, Agent Architectures, Mcp, LangChain, Open Source Llms, Python, Reinforcement Learning, RLHF, Ai Governance
Similar jobs
ML Engineering jobs
Baselayer
San Francisco, CA
Senior AI Engineer responsible for production LLM agents that enrich business identity data through web discovery, verification, classification, and risk scoring. The role requires strong asynchronous Python, agent and evaluation expertise, browser automation, and experience operating AI systems in production.
Otter
Mountain View, CA
Leads the development and production deployment of large-scale ASR and TTS systems for conversational intelligence products. The role requires 5+ years of industry experience, deep speech-model expertise, and strong software engineering and ML operations capabilities.
Ambience Healthcare
San Francisco, CA
Build and improve production AI systems for clinical products, owning evaluations, model behavior, agentic workflows, data flywheels, deployment, and observability. The role requires 5+ years of production ML or applied AI experience, strong Python and modern ML framework skills, and hands-on debugging expertise.
Dialpad
United States
Leads development of speech models, decoders, and low-latency inference systems for next-generation voice agents. Requires 5+ years in speech ML or related audio AI, strong Python and PyTorch experience, and the ability to guide technical direction and mentor engineers.
Ontario, Canada
Design, build, and deploy production ML systems for recommendations, search, ranking, and advertising at internet scale. Own the full ML lifecycle from modeling to monitoring with strong cross-functional collaboration.