AI Workflow Engineer
Skills
Web ApplicationsAdoptionAnalyticsContextLarge Language ModelsReadinessRewriting
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- 2-3+ years of hands-on experience building with AI/LLM systems in production settings
- 2+ years designing and operating agentic workflows and pipelines including multi-step agent orchestration tool use evaluation and governance
- Experience embedded in modern development teams practicing GitOps and CI/CD
- Working knowledge of grounded AI patterns RAG context engineering structured outputs and evaluation observability for LLM systems
- Solid software engineering fundamentals in JavaScript/TypeScript and/or Python
- Comfort working with Angular React and Express web applications
- Alignment with continuous delivery culture and blameless learning practices
- Self-starter bias for action with ability to build measure and share improvements
- Strong communication skills for technical and executive audiences
- Must be authorized to work in the United States and role is US-remote
Nice to have
- Platform engineering
- DevEx focus
- AI governance
- Secure SDLC
- Delivery metrics
- Engineering analytics
Day to day
- Design and implement agentic workflows across the full software development lifecycle, including planning code generation review testing documentation and release.
- Build retrieval-grounded and context-aware AI systems that produce reliable verifiable results and create evaluation harnesses to measure performance.
- Establish governance guardrails human-in-the-loop checkpoints audit trails and security controls for responsible agentic AI adoption.
- Formalize successful workflows into repeatable frameworks playbooks and internal platforms that scale AI acceleration across engineering and the organization.