Hire LLM Engineers
Who Turn Prompts, Retrieval, and Tool Use into Reliable Systems

You can usually start the hiring conversation immediately and receive a shortlist within 24 hours after discovery. For this role, discovery focuses on the LLM behavior you need to improve: prompts, retrieval, tool calling, structured output, model routing, fine-tuning questions, evals, latency, cost, safety, and production failures. That lets us shortlist engineers who match the actual system risk instead of sending generic AI resumes.

Yes. You interview shortlisted engineers before committing. We recommend using a practical scenario: ask the candidate to debug a weak RAG answer, design an eval set, compare model options, add structured outputs to a workflow, protect a tool call, reduce token cost, or decide whether a problem needs prompt iteration, retrieval cleanup, fine-tuning, or a different model. Strong candidates make tradeoffs explicit and avoid treating every LLM issue as a prompt issue.

The first week should produce evidence tied to a real LLM path. You might see an eval dataset, prompt or schema revision, retrieval-quality diagnosis, model comparison, tool-call safety design, production trace review, latency and token-cost report, or a branch that improves one workflow. The important proof is that the engineer can make model behavior more measurable and explain the remaining risks before the work scales.

A strong LLM Engineer should deliver a tested feature path that can survive model, prompt, data, and user changes. Outcomes should include better groundedness, higher task completion, fewer regressions, clearer refusal behavior, valid structured outputs, safer tool use, lower cost per successful task, acceptable latency, and enough tracing for engineers to diagnose failures. The work should be measurable through evals and production observations, not judged by demo quality alone.

Quality is managed through role-specific screening, eval-focused interviews, architecture review, code review, documented decisions, and delivery checkpoints. We look for practical LLM experience across prompt design, structured outputs, retrieval, tool calling, model selection, tracing, eval datasets, regression testing, cost analysis, and safety controls. We also expect production caution: the engineer should know what can be deterministic, what must be measured statistically, and where human review or approval is required.

Yes. The engineer can join your repositories, issue tracker, standups, design reviews, model provider accounts, vector stores, observability tools, eval workflow, and deployment process. We define the operating model early so prompts, schemas, retrieval changes, eval cases, model decisions, tool permissions, and access to sensitive data are versioned and reviewable.

Yes. Devlyn plans overlap windows for interviews, standups, architecture reviews, eval reviews, incident discussions, and release planning. For LLM Engineering, overlap matters because product, engineering, security, data, and support teams often need to align on the same failure mode. We keep the cadence tied to evidence: eval results, traces, cost, latency, regressions, and unresolved release risks.

NDA and IP assignment are handled before onboarding. Access is scoped to the repositories, prompts, eval datasets, model providers, vector stores, source documents, traces, logs, tools, and environments required for the engagement. Because LLM systems can expose sensitive data through prompts, retrieval context, uploaded files, logs, embeddings, and tool calls, the engineer works within your security rules, audit expectations, retention policy, and approval process.

Use the risk-free trial to evaluate whether the engineer can improve a real LLM workflow, communicate tradeoffs clearly, design useful evals, debug retrieval or prompt failures, reason about tool safety, and avoid hidden regressions. 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.

Yes. You can start with one senior LLM Engineer, then expand depending on the surface area. Common additions include a retrieval engineer for RAG quality, a knowledge engineer for domain modeling, an AI application engineer for product integration, a platform engineer for model gateways and observability, a security engineer for sensitive workflows, or a data engineer for ingestion and quality.

Typical options include a retrieval plus LLM prototype, a dedicated senior LLM Engineer, or an LLM plus retrieval engineering pair. The right model depends on whether you need a pilot, production hardening, ongoing feature ownership, model migration, eval infrastructure, or a larger AI product build. We confirm scope after discovery so pricing maps to the proof and operating support you need.

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, eval review, technical reporting, and senior architecture review. For LLM work, management is useful when it keeps product goals, model behavior, retrieval quality, security, and cost decisions tied to the same release path.

LLM Engineers are hard to screen because the role sits between software engineering, AI behavior, retrieval, evaluation, security, product judgement, and cost management. A candidate may be fluent with model APIs but weak on evals, or strong on prompts but unable to ship reliable tool use. Devlyn reduces the screening burden and gives you a trial structure focused on evidence: can the engineer improve the actual LLM path inside your environment?

Devlyn is a better fit when the LLM work affects production systems, customer workflows, sensitive data, security review, model cost, or long-term maintainability. A freelancer can help with a narrow prototype, but production LLM work needs evals, review, versioning, replacement support, IP protection, and continuity. You are reducing the risk that a promising model demo becomes an expensive, untestable system.

This role is best suited for AI assistants, document intelligence, agentic automation, RAG systems, structured extraction, summarization, classification, customer support copilots, internal knowledge assistants, model migration, prompt and eval infrastructure, and production hardening of LLM features. If the work is mostly data platform engineering, model research, GPU infrastructure, or UI implementation, we may recommend a more specialized role instead.