Enterprise AI Agent Systems Engineer (Onsite SF)

MaxIT Consulting - Max Corporate Group; San Francisco (CA); On-site; USD 120,000 - 170,000; Full time

Job summary

MaxIT Consulting - Max Corporate Group in San Francisco seeks a highly technical Forward Deployed AI Engineer – Agent Systems to build and deploy advanced agent systems for large enterprise environments. You will own production deployments and collaborate with customer researchers and engineers to translate workflows into robust AI systems.

The role blends software engineering with applied AI research, requiring hands-on coding, production readiness, and the ability to learn new stacks as needed

Qualifications

• Strong software engineering fundamentals and ability to ship production systems.
• Hands-on experience building agent systems or forward-deployed AI solutions.
• Ability to personally write and own production code.

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 issues originate in model, environment, or evaluation framework.
• Translate customer workflows into robust AI systems.
• Own deployments from implementation to production use.

Skills

Software engineering fundamentals

Agent systems development

Production code ownership

Evaluation & reliability

Cross-stack learning

Stakeholder communication

Job description

MaxIT Consulting - Max Corporate Group in San Francisco seeks a highly technical Forward Deployed AI Engineer – Agent Systems to build and deploy advanced agent systems for large enterprise environments. You will own production deployments and collaborate with customer researchers and engineers to translate workflows into robust AI systems.

The role blends software engineering with applied AI research, requiring hands-on coding, production readiness, and the ability to learn new stacks as needed