Production AI Agent Infra Lead: LLMs, Memory, Eval

Accrescent Group; San Francisco (CA); On-site; USD 140,000 - 220,000; Full time

Job summary

Accrescent Group is seeking an Applied AI Engineer to build and maintain production-grade agent infrastructure at an early-stage company. This role focuses on developing reliable agent systems that execute tasks across iterations, retain long-term context, recover from failures, and include human oversight when needed.

You will own the agentic infrastructure, ship to production in weeks, and define how agents plan, remember, fail, and recover.

Qualifications

• Shipped LLM agents that ran unattended in production for real users.
• Built eval harnesses that people actually used to make shipping decisions.
• Experience with retrieval and memory systems that work on real, messy operational data.

Responsibilities

• Build and maintain the core infrastructure that enables agents to execute tasks reliably, even after dozens of iterations.
• Design and implement memory systems that retain months of client context from messy operational data.
• Develop eval harnesses that teams actually trust to decide what ships to production.
• Own the full loop: build it, measure it, break it, fix it, and make it learn.
• Ship agent systems for real customers and iterate based on production feedback.
• Handle whatever needs doing, infrastructure, orchestration, customer collaboration, and hiring.

Skills

Python

LLM agents in production

Memory systems

Retrieval augmented

Agent orchestration

Infrastructure

Job description

Accrescent Group is seeking an Applied AI Engineer to build and maintain production-grade agent infrastructure at an early-stage company. This role focuses on developing reliable agent systems that execute tasks across iterations, retain long-term context, recover from failures, and include human oversight when needed.

You will own the agentic infrastructure, ship to production in weeks, and define how agents plan, remember, fail, and recover.