Drimva

Sierra

Strategist, Agent Development - Retail

$200K–$290K

San Francisco, CA +1 · On-site · Full-time · Posted Sep 23, 2026

Location
San Francisco, CA
New York, NY
Education
A degree in Computer Science, Engineering, Mathematics, a related technical field, or equivalent practical experience — and/or an MBA or comparable experience at the intersection of technology and business.

About the job

Own the development and launch of customer-facing AI agents for retail clients, translating business goals into production experiences and iterating to improve business outcomes.

What you'll do

  • Manage agent development from scoping and conversation design through tooling, evaluation, launch, and ongoing production iteration.
  • Advise enterprise customers on AI strategy, lead day-to-day working sessions, and keep agent builds on track.
  • Share customer needs with product, research, and platform teams to inform product direction.

What they ask for

  • Strong customer-facing relationship building and communication across technical and non-technical audiences.
  • Analytical problem-solving in ambiguous settings and ability to identify risks and adapt.
  • Technical aptitude, including reading API documentation and understanding data models, system architecture, and engineering tradeoffs.
  • Track record of managing multiple complex, high-visibility projects and driving outcomes.

Nice to have

  • Experience in e-commerce, retail technology, marketplaces, or consumer brands; familiarity with commerce and CX stacks.
  • Production AI/LLM agent experience; founder, founding-team, or operator experience; or a relevant technical degree, equivalent practical experience, MBA, or comparable technology-business experience.

Pay

$200K – $290K USD base salary

Tools

API documentation, RAG, prompt engineering, eval frameworks, agent tooling, voice, SMS

AI focus

Build and deploy customer-facing AI agents for retail, including shopping, support, order management, and outbound use cases; evaluate and continuously improve agents in production.

Model-written summary · the original posting is authoritative

First seen Oct 10, 2026 · Last verified Nov 6, 2026 (UTC)