Hire Context Engineers
Who Control What the Model Sees

You can usually start the hiring conversation immediately and receive a shortlist within 24 hours after we understand your AI product, agent workflow, retrieval stack, memory design, knowledge sources, failure cases, timeline, and seniority needs. The goal is not to send resumes quickly. It is to send Context Engineers who can reason about the information your model sees and the failure modes your users already experience.

Yes. You interview the shortlisted engineers before committing. We recommend using a real AI failure in the interview: a hallucinated answer, a bloated prompt, a missing citation, a stale memory, a bad tool call, or an overlong conversation that lost the user goal. Ask the engineer to explain which context should be added, removed, summarized, retrieved, remembered, or blocked.

The first week should produce visible proof that the engineer understands your context layer. You should see a trace review, context contract, retrieval or memory fix, token budget recommendation, summary format, evaluation slice, or before-and-after response path based on your real failure cases. If progress is unclear, you should know that during the trial, not after a long contract cycle.

A Context Engineer designs how an AI system assembles information for the model. That includes system instructions, recent conversation state, retrieved documents, metadata, memory, tool results, response formats, user permissions, summaries, and uncertainty rules. The role is different from general prompt writing because it owns the full context pipeline that affects reliability, cost, privacy, and answer quality.

Quality is managed through senior screening, role-specific interview criteria, architecture review, trace review, eval design, and delivery checkpoints. We look for practical judgment across memory scope, retrieval quality, context packing, metadata, reranking, token budgets, summarization, tool schemas, source permissions, privacy boundaries, and measurable answer quality.

Yes. The engineer joins your repositories, prompts, evals, traces, retrieval indexes, vector databases, product analytics, support tools, issue tracker, standups, and review process at the access level you approve. The operating model defines who owns sources, who can change memory writes, how evals are reviewed, and how context changes are released.

Yes. Devlyn works with distributed teams and plans overlap windows for interviews, standups, trace reviews, eval reviews, and escalation. For Context Engineer engagements, the communication rhythm is tied to proof points that matter: context relevance, citation quality, retrieval precision, hallucination reduction, token efficiency, latency, and user-visible answer usefulness.

NDA and IP assignment are handled before onboarding. Access is scoped to the repositories, prompts, traces, knowledge sources, vector indexes, memory stores, and evaluation datasets required for the scope. Sensitive context work follows your security rules for source permissions, tenant isolation, redaction, retention, audit logs, and approval workflows.

Use the risk-free trial to evaluate whether the engineer can understand your AI failures, inspect traces, communicate tradeoffs, and improve context quality without creating privacy or product risk. If the fit is wrong, we replace the engineer within 48 hours instead of forcing you through a long notice period or another sourcing cycle.

You can start with one specialist and expand only if the scope requires it. Common expansion paths include Retrieval Engineers for RAG quality, LLM Engineers for model behavior and orchestration, AI Product Engineers for user experience, Data Engineers for source pipelines, Security Engineers for sensitive data controls, and Platform Engineers for observability and deployment.

Typical options include a Context Architecture Sprint, a dedicated Senior Context Engineer, or a Context plus Retrieval plus LLM Engineering pod for larger production systems. We confirm the model after discovery so you can compare a focused audit, a dedicated hire, or a small pod against the actual risk: hallucination, memory leakage, bad retrieval, token cost, long-running agent failure, or missing evaluations.

We can support both models. If you already have strong product and engineering leadership, the engineer can plug into your process. If you need more structure, Devlyn can add delivery oversight, sprint planning, reporting, and senior technical review around context contracts, memory policy, retrieval tuning, trace review, eval design, and rollout checkpoints.

Devlyn reduces the hidden work of sourcing, vetting, onboarding, replacing, and governing specialist AI talent. That matters for Context Engineers because the risk is often hard to spot in a resume: the model looks capable, but it fails because the wrong context is retrieved, too much history is carried forward, memory is unsafe, or traces cannot explain the answer. You get a shorter path to qualified candidates and a trial focused on technical proof.

Devlyn is a better fit when context work affects production answers, customer trust, sensitive data, tool execution, cloud cost, or long-term product quality. You get vetting, replacement support, delivery governance, IP protection, and continuity around outcomes like context contracts, safe memory, measurable retrieval, token budgets, traceability, and maintainable agent behavior.

The strongest fit is work where AI quality depends on the right information reaching the model. Common examples include long-running customer support agents, sales copilots, enterprise RAG over policies or tickets, coding agents over large repositories, personalization memory, agent handoff summaries, tool context design, source citation, prompt compression, retrieval evaluation, and privacy-safe memory for multi-tenant products.