AI Engineer II

The AI Engineer II independently develops and supports AI engineering capabilities that enable reusable, secure, and scalable AI-assisted software delivery. This role builds components of the AI Harness, agent frameworks, evaluation systems, and developer workflow tools, while partnering with engineering teams to translate practical delivery needs into reliable AI-enabled solutions. The AI Engineer II contributes to production-quality AI applications and helps improve the consistency, safety, and usefulness of AI-supported engineering workflows.

Develop AI Harness & Platform Capabilities

• Design, build, test, and maintain well‑defined AI platform components, services, connectors, and internal tools.
• Implement model routing, prompt execution patterns, API integrations, and reusable utilities aligned to platform standards.
• Improve reliability, performance, observability, and usability of AI engineering tools.
• Support production operations for AI‑enabled services, including troubleshooting, monitoring, and issue remediation.
• May be responsible for on‑call rotation and front‑line incident response for AI‑enabled production services, including triage, escalation, and remediation.

Build Reusable Agents, Skills & Connectors

• Develop reusable agents, skills, workflow components, and connectors that can be used across engineering teams.
• Implement agent behaviors, tool calls, structured workflows, and integration logic that support AI‑assisted SDLC use cases.
• Apply standards for modularity, permissions, traceability, and maintainability.
• Partner with more senior engineers to refine shared libraries and reduce duplicate AI engineering work across teams.

Implement Evaluation, Testing & LLMOps Workflows

• Build and maintain evaluation workflows for prompts, agents, tools, and AI‑generated outputs.
• Develop automated tests, regression checks, quality benchmarks, and validation approaches for AI‑enabled capabilities.
• Support versioning, release management, rollback readiness, and lifecycle management for prompts, agents, and AI components.
• Analyze model and agent behavior to identify quality, cost, latency, security, or reliability concerns.

Apply AI Security & Governance Controls

• Embed approved logging, access control, data classification, and risk controls into AI‑enabled workflows.
• Work with security and governance partners to address implementation requirements for responsible AI use.
• Validate that AI tools and agents are handling sensitive data appropriately and operating within defined guardrails.
• Support remediation of security, compliance, or audit findings related to AI engineering solutions.

Partner with Engineering Teams on AI‑Assisted Delivery

• Collaborate with software engineering teams to understand delivery pain points and identify AI‑enabled workflow improvements.
• Build examples, templates, and golden‑path patterns that accelerate adoption of shared AI engineering capabilities.
• Support onboarding, technical guidance, and issue resolution for engineering users of AI platform tools.
• Contribute to team standards, documentation, and communities of practice around AI‑assisted development.

• Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Information Systems, or a related technical field, or equivalent work experience, with typically 5+ years of experience in software engineering, AI engineering, ML engineering, platform engineering, automation, or developer productivity roles.
• Strong programming skills are required, preferably in Python and one or more modern application development languages.
• Candidates should have experience building APIs, services, integrations, automated tests, and cloud‑based applications.
• Experience with LLM…