Agent Engineer
At a glance
Mid-level AI Engineering role at Clera. Remote, United States · full-time · no salary stated · we estimate $131,000–$205,000 across the United States.
Clera has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Turn useful advances in agent technology into product ideas, then shape model behaviors into reusable workflows for users
- Build Python agent infrastructure for orchestration, context handling, structured outputs, retries and recovery from errors
- Create integrations for tools such as email, calendars, browsers, documents, CRMs and business software
- Develop agent capabilities across Django services, APIs, React interfaces, data models and background jobs, then run them in production
- Use evaluation findings, production traces and user feedback to improve agent quality, speed, cost, reliability and safety
What you bring
- At least five years building production software end to end, including deployment and ongoing operational improvements
- Strong Python skills plus practical experience with Django, APIs, React and TypeScript
- Hands-on experience building or operating agents with planning, tool use, structured outputs, state handling and orchestration
- Experience designing external API and business-software integrations, and diagnosing complex production systems
Who this fits
This role suits a mid-level engineer who can turn unclear user needs and emerging technology into practical features, and guide technical choices on ambiguous problems. The position is on-site in Palo Alto, with hybrid flexibility.
From the employer
About the Role
Build reliable agent capabilities that turn advances in reasoning, planning, memory, and tool use into useful product experiences. This role spans agent systems, platform engineering, integrations, and full-stack product development.
What You'll Do
Investigate developments in agent technology and identify valuable product opportunities.
Translate promising model behaviors into reusable skills and workflows that address user needs.
Build and improve Python agent infrastructure, including orchestration, context management, structured outputs, retries, and error recovery.
Design tools and integrations for email, calendars, messaging, browsers, documents, CRMs, and business software.
Own capabilities across Python agents, Django services, APIs, React interfaces, data models, background jobs, observability, and production operations.
Create platform abstractions that make capabilities easier to compose, extend, and maintain.
Improve agent execution latency, cost, reliability, and safety.
Work with evaluation and quality partners to define expected behavior and turn evaluation results into engineering improvements.
Analyze production traces and user feedback to identify and resolve underlying issues.
Guide technical decisions on ambiguous problems and contribute to design reviews.
What We're Looking For
At least 5 years of experience building production software systems end to end, including deployment and operational iteration.
Strong Python skills and hands-on experience with Django, APIs, React, and TypeScript.
Experience building or operating agent systems, including planning, tool calling, structured outputs, context and state management, retries, and orchestration.
Experience designing integrations with external APIs and business software.
Ability to turn loosely defined user needs and emerging technologies into practical product features.
Experience owning complex systems in production, including observability, debugging, and improvements based on production signals.
Skill in designing reusable platform abstractions and diagnosing issues across prompts, model outputs, application code, databases, and APIs.
Background in LLMs, prompt engineering, or production model evaluation is useful, as is experience optimizing high-volume systems for latency, cost, and reliability.
Demonstrated career progression within an organization or experience founding a startup, along with strong engineering fundamentals.
Location
On-site in Palo Alto, California, with hybrid flexibility.
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