Forward Deployed AI Engineer – Agents & Research Systems
MaxIT Consulting – Max Corporate Group- Location
- San Francisco, United States
- Workplace
- —
- Employment
- Full Time
- Salary
- —
Posted today
San Francisco, California | Primarily On-site
We are seeking a highly technical Forward Deployed AI Engineer – Agents & Research Systems to build and deploy advanced agent systems for large enterprise environments.
The Opportunity
This role combines strong software engineering with applied AI research judgment. You will work directly with technically sophisticated customers while building the agent systems, data pipelines, post-training workflows, and evaluation infrastructure required for production deployments.
Key Responsibilities
- Build and ship production agent systems for complex enterprise workflows.
- Develop data pipelines, orchestration systems, and evaluation harnesses.
- Run and analyze post-training workflows for customer-specific models and agents.
- Work directly with customer researchers and engineers.
- Evaluate whether performance issues originate in the model, environment, or evaluation framework.
- Translate customer workflows into robust, testable AI systems.
- Own technical deployments from implementation through production use.
Required Qualifications
- Strong software engineering fundamentals and demonstrated ability to ship production systems.
- Hands-on experience building agent systems, AI applications, or technical forward-deployed solutions.
- Ability to personally write and own production code.
- Strong understanding of evaluation, agent behavior, and production reliability.
- Ability to work across technologies and learn new stacks as required by the problem.
- Strong communication skills with technical enterprise stakeholders.
Seniority
The search is open from exceptional new graduates and early-career engineers through experienced senior engineers. Technical ability and demonstrated ownership are more important than years of experience.
Work Arrangement
The team is based in San Francisco and strongly prefers in-person collaboration. Limited flexibility may be considered for exceptional candidates, particularly experienced candidates elsewhere in the United States who can spend an initial period working on site.
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