Senior Technical Writer

BCforward is seeking a highly motivated and experienced Senior Technical Writer

Note: Candidate must be local to Michigan and willing to work on W2.

Job Title: Senior Technical Writer

Duration: 6 months Contract

Job Location: Farmington Hills, Michigan 48331-3552

Pay Rate: $55/W2

Must Have

3-4 years of Document Control experience

Author

Taxonomy Development

Technical Manuals

Vocabulary

Nice To Have

Markdown Management

Model Context Protocol

YAML

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

Toolchain

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