# Applied AI Engineer
Join myTomorrows as an Applied AI Engineer to help the organization become AI-native. This role focuses on internal impact: building production-capable AI-enabled tools, workflow automations, agents, and integrations that solve real business problems across Operations, Regulatory, Commercial, and Marketing teams.
## Key Responsibilities
- Build production-capable AI-enabled internal tools, workflow automations, agents, and integrations by embedding with internal teams
- Use modern LLM capabilities including structured outputs, tool calling, retrieval-augmented generation, agentic workflows, and prompt engineering
- Help teams translate ambiguous business problems into clear, testable AI-assisted delivery plans
- Build backend services, APIs, integrations, and internal applications using modern software engineering practices
- Work with Product, Engineering, QA, DevOps, Data, Legal, Privacy, and Security teams to ensure AI systems are safe, secure, observable, and maintainable
- Design and implement evaluation approaches for AI systems, including test sets, human review loops, quality criteria, and monitoring
- Create reusable playbooks, templates, prompts, and examples to help other teams adopt AI effectively
- Stay updated with the evolving AI engineering landscape and translate developments into practical opportunities
## Working Model
You will work as a "Forward Deployed Engineer" following a structured engagement model:
- Insertion: Embed with teams doing actual work, map current workflows, tools, and pain points
- Discovery: Identify highest-leverage intervention points for maximum impact
- Delivery: Build production-ready solutions with measurable success criteria and identify an internal champion
- Handoff: Leave behind a running system, evaluation framework, and reusable patterns
## Success Criteria (First 6 Months)
- Deliver 2-3 high-impact AI Acceleration missions
- Ship production-capable software with clear ownership, tests, and documentation
- Create reusable templates and examples for other teams
- Help teams understand where AI is genuinely useful and what guardrails are needed
- Contribute to a culture judging AI-assisted work by production outcomes