Production AI Deployment Engineer - Onsite in 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, CA seeks a highly technical Applied AI Deployment Engineer to build and deploy advanced agent systems for large enterprise environments.

You will blend software engineering with applied AI research, building agent systems, data pipelines, post-training workflows, and evaluation infrastructure for production deployments, collaborating directly with customer researchers and engineers, with a strong in-person collaboration preference.

Qualifications

• Strong software engineering fundamentals and ability to ship production systems.
• Hands-on experience building agent systems or AI applications.
• Ability to write and own production code.
• Strong understanding of evaluation, agent behavior, and production reliability.
• Able to learn new stacks across technologies.
• Strong communication skills with technical enterprise stakeholders.

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.

Skills

Software engineering

Agent systems

Production code

Evaluation frameworks

Enterprise stakeholders communication

Job description

MaxIT Consulting - Max Corporate Group in San Francisco, CA seeks a highly technical Applied AI Deployment Engineer to build and deploy advanced agent systems for large enterprise environments.

You will blend software engineering with applied AI research, building agent systems, data pipelines, post-training workflows, and evaluation infrastructure for production deployments, collaborating directly with customer researchers and engineers, with a strong in-person collaboration preference.