Applied AI Engineer (Enterprise)

Quick Facts

• Role: Applied AI Engineer (Enterprise)

Description

Serve as a technical partner to enterprise and educational institutions, helping turn AI use cases into durable production systems and adoption at scale. You’ll design, build, evaluate, debug, and deploy AI systems, making technical decisions across model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. You’ll also turn deployment experience into product feedback and reusable deployment patterns.

Responsibilities

• Hands-on coding to design, build, evaluate, and deploy AI systems from prototype to reliable production

• Partner with enterprise customers to identify high-value opportunities and define architectures, implementation plans, evaluation strategies, and success criteria

• Make sound technical decisions across model selection, agent and tool design, retrieval and context design, data pipelines, evaluation, reliability, observability, latency, cost, safety, security, and governance

• Diagnose complex implementation challenges, reproduce failures, run tests, and drive blockers to resolution

• Collaborate with customer engineering teams and internal teams to convert deployment learnings into product feedback

• Create reusable architectures, tooling, playbooks, and technical guidance for future enterprise deployments

Requirements

• Personally designed, built, and shipped AI/ML systems in enterprise environments from prototype to production

• Highly proficient in Python; comfortable working end to end across model APIs, agent/tool development, retrieval, data pipelines, evaluation, observability, integrations, and production operations

• Experience evaluating AI systems systematically using representative data, automated graders, production signals, and human judgment

• Experience navigating enterprise production requirements: integrations, reliability, observability, security, privacy, data governance, performance, and cost

• Experience (valuable) in education or research environments, including institutional data, privacy, access controls, and complex integrations

• Ability to connect technical decisions to customer workflows, adoption, and measurable business outcomes

• Clear and credible communication with engineers, technical leaders, security/product teams, and executives

• End-to-end ownership, strong technical judgment, and effective work through ambiguity

Benefits

• Hybrid work model: three days per week in the office for office-based employees; remote option available

• Relocation assistance for new employees