Strategist, Agent Development (spanish Speaking)

Sierra Sierra · AI Frontier · San Francisco, CA · Product

This role focuses on owning the end-to-end development lifecycle of AI agents for enterprise customers, from initial scoping and design to production launch and iteration. It involves translating business goals into functional agents, acting as a strategic advisor to clients, partnering with customer teams, and providing product feedback. The role sits at the intersection of product, agent building, and customer-facing work, requiring technical aptitude and a focus on measurable business outcomes.

What you'd actually do

  1. Own the Agent Development Life Cycle. Take agents from initial scope through conversation design, tooling, and evaluation, to launch and continuous iteration in production — translating business goals into an agent that outperforms, and tuning relentlessly until it’s the best version of itself in the real world. You’ll have real ownership and autonomy over the outcome.
  2. Be the trusted strategic advisor to enterprise leaders. Sit shoulder-to-shoulder with our customers’ executives, understand what success actually means to their business, and shape the AI strategy that gets them there.
  3. Partner with the customer’s working teams day to day. Run working sessions, troubleshoot the agent as issues come up, and make the calls that keep the build on track for launch.
  4. Directly impact product. Surface unmet needs from your customer work, and feed them back to research and platform to shape where Sierra goes next.

Skills

Required

  • Customer obsession. Genuine energy being in front of customers — understanding their business and needs, thriving in complex organizations, and building trusted relationships.
  • Communication. Clear, direct, and persuasive across technical and non-technical audiences.
  • Strategic problem-solving in ambiguity. Strong analytical and critical-thinking skills to break down complex problems, spot risks early, and adapt as things change fast.
  • Technical aptitude. Comfortable engaging directly with technical concepts - reading API documentation, understanding data models and system architecture, and following engineering tradeoffs in a design discussion.
  • Ownership and delivery. A track record of running multiple complex, high-visibility projects at once and driving them to outcomes.

Nice to have

  • Experience building or deploying AI/LLM agents in production — hands-on with the tools behind them (eval frameworks, agent tooling, RAG, prompt engineering) — working closely with technical counterparts.
  • You’ve been a founder, founding team member, or operator — you know how to balance craft, ownership, and speed.
  • 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.
  • Experience in financial technology, implementations at financial institutions, working inside of these institutions or familiarity with financial services security and compliance requirements (fair lending, PII handling, etc.) are a strong plus.

What the JD emphasized

  • production agent handling thousands of conversations a day
  • outperforms human agents
  • consistently outperforms human agents
  • handle complex, regulated interactions
  • multi-team production deployment
  • measurable lift in outcome week over week
  • building or deploying AI/LLM agents in production
  • hands-on with the tools behind them (eval frameworks, agent tooling, RAG, prompt engineering)

Other signals

  • building agents
  • customer-facing work
  • production agent handling thousands of conversations
  • translating business goals into an agent
  • tuning relentlessly
  • shaping the AI strategy
  • troubleshoot the agent
  • make the calls that keep the build on track
  • surface unmet needs
  • feed them back to research and platform
  • launch a subscription-retention agent
  • stand up an agent for a commerce customer
  • redesign the conversation flows for a financial services agent
  • handle complex, regulated interactions
  • partner with product on a net-new capability
  • define the evaluation and quality bar for a new agent
  • drive a measurable lift in outcome
  • building or deploying AI/LLM agents in production
  • hands-on with the tools behind them (eval frameworks, agent tooling, RAG, prompt engineering)