AI Technical Writer

Senior Technical Writer

Location: Farmington Hills, MI Contract: 5+ Months (Long Term Contract) Working Mode: Hybrid
Experience: 5 - 7 years Must Have:
3-4 years of Document Control experience
Author
Taxonomy Development
Technical Manuals
Vocabulary
Summary: We are looking for a Senior Technical Writer to join our AI agentic engineering team.
You will develop and manage the knowledge catalog that our production AI agents read from — authoring the entries that encode how our IT enterprise actually works, and structuring them so agents retrieve the right knowledge at the right moment.
This is a writing role at its core, but the reader is different.
Your audience is an AI agent operating under a context budget, and behind it, the engineer who has to trust what that agent produces.
Success is measured less by page count than by whether agents behave correctly because your entries were clear, correctly scoped, and easy to find.
What You'll Do:
Author knowledge entries — service overviews, runbooks, decision records, schemas, specifications, and glossary terms — drawn from workflows, policies, engineering standards, and technical documentation
Write the short descriptions and summaries that drive progressive disclosure — the text an agent reads to decide whether loading the full entry is worth the context it costs
Structure orientation paths so an agent onboarding to an unfamiliar domain encounters the mental model, entry points, and invariants first, and drills into detail only on demand
Maintain hierarchy coherence across a growing catalog — cross-linking, indexing, reachability, and the layered structure that keeps entries discoverable as the corpus scales
Own the scoping metadata that routes entries to the right consumer in the right context — precision here determines whether an agent gets the relevant standard or the wrong one
Author and maintain prompt assets — reusable skills, agent instructions, and reference material consumed directly by production agents
Codify engineering constitutions — turn review standards, security requirements, and platform conventions into structured, versioned rules that automated code review agents apply on every pull request
Interview developers and engineers to capture the rationale behind decisions — the "why" is rarely present in the source material, and it is usually the part that matters most
Advance entries through review and approval with engineering and governance partners, so only verified content reaches production consumers
Govern vocabulary — maintain the glossary and naming conventions, resolving terminology collisions before they propagate into schemas, tooling, and agent behavior
Keep the catalog current — identify and retire stale entries as the systems they describe change
Core Capabilities:
Writes with precision under hard length constraints, where an unnecessary sentence carries a measurable cost
Structures information for machine consumption as fluently as for human readers
Reads primary source material — code, configuration, infrastructure definitions, runbooks — accurately enough to summarize it without an engineer rewriting the result
Interviews technical staff effectively and recognizes when an answer is incomplete
Sustains consistency of voice, terminology, and structure across a large, cross-linked corpus
Exercises editorial judgment about what belongs in the catalog, what should be merged, and what should be removed
Partners credibly with developers, engineers, and governance teams without needing content pre-digested
What You Will Bring:
5–7 years of technical writing experience in software, platform, or infrastructure environments
Demonstrated ability to produce accurate technical content from primary sources — code, configuration, specifications, runbooks — with limited hand-holding
Strong information architecture instincts: taxonomy, cross-linking, layered structure, and progressive disclosure
Experience authoring reference material, standards, or specification documentation consumed by engineers
Comfort working directly with developers and engineers, including the credibility to push back when source material is incomplete or contradictory
Experience partnering with governance, risk, security, or compliance stakeholders
Editorial discipline — consistency of terminology, voice, and structure maintained across many documents and contributors
Familiarity with docs-as-code practice: Markdown, version control, and content that lives in a repository alongside the systems it describes
Clear verbal communication — much of the source material is gathered in conversation, not handed over
Nice to Have:
Hands-on experience writing prompts, agent instructions, or reusable skill definitions for LLM-based systems
Understanding of context management techniques for AI agents — progressive disclosure, context window economics, retrieval strategy
Experience with structured content — YAML or JSON frontmatter validated against a schema, content models, or typed document formats
Working comfort with Git and CLI-based authoring workflows — pull requests, validation tooling, link checking, and CI feedback
Exposure to taxonomy design, ontologies, knowledge graphs, or controlled vocabularies
Background in developer documentation, API documentation, or internal platform documentation
Familiarity with AI agent concepts — tool use, orchestration, retrieval, and agent context protocols such as MCP
Reading-level familiarity with Python, TypeScript, Terraform, or YAML-based configuration
Experience in banking, financial services, or another regulated industry