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