Technical writer

Seeking  to fill in a Technical writer  for   immediate onsite  contract opportunity with  my direct client  in Farmington Hills  MI

If interested , share your word document resume, work authorization, expected hourly pay rate, Farmington Hills (current) location address , availability for onsite interview and  work, skill stack and LinkedIn.

Reach me for more details. thanks.

If not, Appreciate any referrals. 

Toolchain

Markdown · YAML · structured frontmatter and schema validation · Git and pull-request workflows · documentation validation and link-checking tooling · LLM agent platforms · MCP

Work Environment

Hybrid. Candidates must be located in the Farmington Hills / Metro Detroit, Michigan area. No remote candidates.

TECHNICAL SKILLS

Must Have:

3-4 years of Document Control experience

Author

Taxonomy Development

Technical Manuals

Vocabulary

Markdown Management

Model Context Protocol

YAML

LOCATION INFORMATION

Comerica Great Lakes Campus

36455 Corporate Drive

Farmington Hills Michigan 48331-3552

JOB SUMMARY

Senior Technical Writer — Agen

JOB DESCRIPTION

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 Differentiates This Role

Most technical writing roles optimize for a human who is scanning a page. This one optimizes for an agent deciding what to load, and a governance team deciding whether to trust it. A description is a retrieval decision. An index is a retrieval surface. A vague entry does not merely confuse a reader — it produces a wrong answer in a production system.

You will also write the prompt-side assets, not just the reference material, which means the boundary between "documentation" and "how the agent thinks" is genuinely yours to manage. Writers who thrive here think in information architecture first, care about how knowledge is consumed rather than only how it is published, and are comfortable being accountable for downstream agent behavior.

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