Model behavior and evals
Hire LLM Engineers who make model behavior measurable, reliable, and cost-aware.
Get a dedicated LLM Engineer to evaluate prompts, compare models, design routing, reduce latency/cost, and decide when fine-tuning, RAG, or orchestration is actually needed. Shortlist in 48 hours. Two-week paid trial in your codebase. Starts at $2,500/mo.
Direct answer
What does LLM Engineer own?
An LLM Engineer is the right hire when model behavior needs to become measurable instead of subjective. This role owns prompt/version strategy, evaluation datasets, model comparison, routing, latency, cost, regression testing, and decisions about whether to use prompting, RAG, fine-tuning, or orchestration.
Hiring problem
Hire this role when model quality, routing, and cost are being managed by instinct.
Teams ship prompt changes by feel, switch models without baselines, and cannot explain whether quality improved, cost rose, or regressions appeared.
- Prompt/version strategy
- Evaluation datasets
- Model comparison
- LLM routing
- Fine-tuning decision support
- Latency/cost analysis
- Regression testing
- Failure taxonomy
- Inference quality monitoring
- Building only the frontend app layer
- Pure document ingestion/RAG architecture
- Generic data science reporting
- Full enterprise rollout ownership
First 14-day proof
The trial should create evidence, not just activity.
Eval dataset
A representative set of examples, expected behavior, edge cases, and known failures. It matters because every later quality claim is only as trustworthy as the data behind it.
Baseline scorecard
Shows current quality, cost, latency, and known regression areas before any change. You cannot prove improvement without a starting number.
Prompt and model comparison
Compares providers, prompts, temperatures, context strategies, or model versions head to head. A weak version is opinion; a strong version is a table you can defend.
Cost and latency report
Shows where spend and delay come from, and which tradeoffs are safe to make. It turns "AI is getting expensive" into specific, fixable line items.
Failure taxonomy
Groups failures into hallucination, refusal, formatting, reasoning, routing, context, safety, or latency categories so fixes target the real problem, not the loudest one.
Recommendation memo
Explains whether the next move should be prompting, RAG, fine-tuning, routing, caching, platform work, or product UX — with evidence, so the roadmap stops being a guess.
Default stack
Stack fluency for LLM Engineer work.
The exact tools follow your environment. These are the common surfaces we vet against for this role.
Use cases
Where this hire creates leverage.
The best use case is one where the role can own a clear first proof during the paid trial.
LLM quality regression
Model output changed and the team cannot prove why. The first proof is an eval baseline that pins down what actually moved and which prompts or versions caused it.
Model routing
Different tasks need different models based on cost, risk, latency, or accuracy. The engineer designs routing rules backed by a comparison scorecard.
Prompt and eval system
Prompts need versioning, tests, review gates, and rollout discipline. The proof is a repeatable eval harness, not a one-off spreadsheet.
Fine-tune readiness
The team needs to know whether fine-tuning is justified or a distraction. The memo answers it with data before anyone spends on training. Pair with a Platform Engineer if serving infrastructure is involved.
Cost reduction
Spend is rising and quality cannot be sacrificed blindly. The engineer trims context, caches safe paths, and routes tasks while watching the scorecard.
Model migration
The team is moving from one model or provider to another and needs evidence the switch is safe. The proof is a before/after comparison on the eval set.
Transparent pricing
Pick seniority by ownership, not mystery quotes.
Supervised delivery for clear implementation work.
Independent feature ownership for production AI work.
High-judgment ownership for ambiguous or risky AI delivery.
Outcome clarity
What should change after you hire this role?
Quality has a baseline.
Model changes are reviewed against evidence.
Cost and latency are visible before rollout.
Adjacent-role comparison
When another AI role is the better hire.
RAG & Context Engineer
Choose RAG if the issue is grounding over private data.
PLTAI Platform Engineer
Choose AI Platform if model access must be centralized across teams.
APPAI Application Engineer
Choose AI Application if the issue is product UX and integration.
DSCData Scientist
Choose Data Scientist if the question is business measurement rather than model behavior.
Vetting criteria
Screened for this role’s failure modes.
Eval design
Model comparison discipline
Cost and latency judgment
Prompt/version control
Failure taxonomy
Interview questions
Use the interview to test judgment.
- How do you build an eval set from real failures?
- When would you choose routing over fine-tuning?
- How do you measure prompt regression?
- How do you lower cost without hiding quality loss?
Hiring flow
From scope to paid trial.
30-minute role scope
Map the AI workflow, current stack, first deliverable, security boundaries, seniority, and the role that should own the work.
2-3 vetted engineers
Receive a short list with matching rationale. The goal is fewer names with stronger fit, not resume volume.
Paid trial in your codebase
The selected engineer works inside your repo, rituals, issue tracker, and review process so fit is judged by real work.
Continue, replace, pause, or scale
Continue month-to-month, request a free replacement, pause without a long lock-in, or add adjacent roles.
Security, IP, governance
Repo access is scoped before the engineer starts.
NDA, IP assignment, repository access, communication channels, data boundaries, and AI tool rules are clarified before onboarding. Devlyn avoids unverified compliance claims and works within buyer-controlled systems.
FAQ
Questions before you hire LLM Engineer.
Do we need fine-tuning?
Maybe, but not by default. A good LLM Engineer tests prompting, retrieval, routing, and eval coverage before recommending fine-tuning.
How does this role reduce AI cost?
They route tasks, trim context, cache safe paths, compare models, and monitor quality so savings do not create hidden regressions.
What should they produce in two weeks?
An eval baseline, scorecard, model/prompt comparison, cost/latency view, and a clear recommendation for the next architecture move.
How fast can I see LLM Engineer candidates?
After the role scope, Devlyn targets two or three vetted profiles within 48 hours.
What does the two-week paid trial include?
The trial should produce role-specific proof for LLM Engineer work inside your actual repo, data environment, or approved workflow.
Can the engineer work in our repository?
Yes. Repo access, communication channels, data boundaries, NDA, and IP assignment are scoped before onboarding.
What if fit is wrong?
You can request a free replacement instead of being forced through a long lock-in or conversion fee.
What does pricing include?
Pricing covers one dedicated AI-native engineer. Junior starts at $2,500/mo, mid at $3,500/mo, and senior at $4,500/mo.
Final CTA
Tell us the AI workflow. We’ll confirm whether LLM Engineer is the right hire.
If another role is a better fit, the role scope should catch that before you interview anyone.