Senior Applied AI Engineer
Puntt AI ·
senior
Salary Range (USD)
$185k - $210k
Location
San Francisco, USA
Visa Support
Not mentioned
Funding Stage
Unknown
Job Responsibilities
- • owns the agentic core
- • multi-agent orchestration
- • RAG and retrieval
- • stateful async workflows (Temporal)
- • eval frameworks that make compliance AI trustworthy at scale
Required Skills
production engineeringbuilding with LLMs in real applicationsstrong Pythonhands-on RAG
Engineering Culture & Tech Stack
PythonLLMsRAGTemporal
failure modes
observability
reliable
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Puntt AI | Forward Deployed Engineer + Senior Applied AI Engineer (Agentic Systems) | Onsite SF | Full-time
Puntt AI is the system of record for enterprise marketing compliance. Our AI agents turn hours of manual brand and packaging review into minutes of automated precision. Danone, Nestlé, and Kenvue are live customers. The product works. Now we scale.
Forward Deployed Engineer | $190k + equity
https://wellfound.com/l/2CiVyv
The FDE is the person in the room when an enterprise customer shows you their actual broken process. You run discovery, figure out what's really going wrong, configure and ship against it, and bring that signal back to product in a way the team can actually use. You work directly alongside our CEO in live enterprise cycles. What you learn shapes what we build next.
Best fit: solutions engineering, field engineering, or technical founder background. You've been in enterprise rooms, you get to the real problem fast, and you can build the thing that proves the fix. Full stack, AI-native, 7+ years.
Senior Applied AI Engineer | $185k-$210k + 0.75%-1.25% equity
https://wellfound.com/l/2C3WzL
This role owns the agentic core -- multi-agent orchestration, RAG and retrieval, stateful async workflows (Temporal), and the eval frameworks that make compliance AI trustworthy at scale. If you've spent real time thinking about failure modes, observability, and what reliable actually means when LLMs are in the loop, that's who we're looking for.
5-7+ years production engineering. 1-2+ years building with LLMs in real applications. Strong Python. Hands-on RAG.
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Content parsed by LLM from Hacker News raw data. Confidence:HIGH