Hire an LLM Engineer
Makes model behavior measurable, prompts, RAG, evals, and structured outputs that survive production.
Owns everything between the model API and a reliable feature: prompt design, retrieval quality, tool calling, eval datasets, regression testing, and cost-per-successful-task.
30-minute fit call with an engineering lead. No hard sell, no deposit, and no change to the published rates.
MEASURED, NOT VIBES-CHECKED
WHAT THEY OWN
Concrete deliverables, not job-description poetry.
01
Eval infrastructure
Datasets, graders, and regression suites so model changes are measured, not vibes-checked.
02
RAG pipelines that answer correctly
Retrieval diagnosis, chunking, reranking, and groundedness you can prove.
03
Structured outputs & tool calling
Valid schemas, safe tool use, and refusal behavior that holds under pressure.
04
Model selection & routing
The right model per task, quality, latency, and cost traded off explicitly.
05
Token cost & latency control
Lower cost per successful task without silent quality loss.
06
Production tracing
Every failure diagnosable from logs, not reproduced by luck.
HOW MATCHING WORKS
From role brief to production evidence.
You are not buying a resume. You are choosing a specific engineer, then testing the match on committed work before making a longer decision.
01
Map the real gap
A 30-minute call with an engineering lead defines what the LLM Engineer must own, the stack they inherit, and the evidence that will count as a successful trial.
02
Review matched profiles
Within 48 hours, you receive 2-3 role-matched profiles. You can review the work history, technical evidence, certifications, and availability before choosing whom to interview.
03
Interview against the work
We help turn your current failure cases into practical interview scenarios. You choose the engineer; no profile moves forward without your approval.
04
Prove fit in one paid week
The engineer works in your repo or approved data environment. You judge real output, communication, and technical decisions before any month-to-month continuation. Typical evidence includes built a first eval set from your real failure cases, diagnosed your worst retrieval or prompt failure, shipped one measured improvement to a live path.
What makes a shortlist useful: each profile should match the ownership boundary, not merely repeat the right tools. Compare the candidate's recent work, the decisions they owned, the evidence they can explain, and the overlap they can commit to. Ask who reviewed the work and what changed after it reached production. Certifications support that judgment when a platform or security standard matters; they do not replace production experience.
No deposit, no unpaid test project, and no long-term contract required. Trial work is paid at the published rate and belongs to you.
PRICING
Pick the level, keep the senior oversight.
Junior
$3,200 /month
or $20/hr on Time & Material
AI-native from day one
Senior
MOST HIRED$4,800 /month
or $30/hr on Time & Material
Architecture & judgment
Dedicated engineers are billed monthly; Time & Material is billed hourly on tracked actuals. The paid one-week trial applies to every dedicated hire.
YOU NEED THIS ROLE IF
Your AI feature works in the demo and embarrasses you in production
Nobody can say whether last week's prompt change made things better
RAG answers are confident, cited, and wrong
BY END OF WEEK ONE
Built a first eval set from your real failure cases
Diagnosed your worst retrieval or prompt failure
Shipped one measured improvement to a live path
Reported cost, latency, and quality as numbers
OUTCOMES YOU CAN MEASURE
Groundedness and task completion you can chart
Fewer regressions per model or prompt change
Lower cost per successful task
LLM behavior your team can debug without folklore
DEVELOPER PROFILES
See the depth behind a useful shortlist.
Explore LLM Engineer profiles with the work evidence, relevant experience, and skill detail behind a useful shortlist.
Aravind Balakrishnan
Senior LLM Engineer
Senior LLM engineer, 13 years, specializing in RAG retrieval quality and eval-driven model behavior.
Kacper Wiśniewski
Senior LLM Engineer
Senior LLM engineer who makes model behavior measurable, not anecdotal.
Henrique Vasconcelos
Senior LLM Engineer
Senior LLM engineer, 9 years, specializing in structured outputs and tool calling.
Tomás Ferreyra
Senior LLM Engineer
Senior LLM engineer specializing in model routing and cost control.
Youssef Adel Kamel
LLM Engineer, Multimodal AI
Junior LLM Engineer specializing in multimodal features across vision, documents, and text.
Chukwuemeka Okafor
LLM Engineer, Fine-Tuning & Distillation
Junior LLM Engineer specializing in fine-tuning and distillation: compresses frontier-model quality into small open-weight models, cutting inference cost by up to 71% while holding eval accuracy within two points.
Karlo Reyes Mendoza
LLM Engineer
Junior LLM Engineer specializing in production tracing and observability for RAG and agentic systems.
Mariana Sequeira
LLM Engineer
Junior LLM Engineer focused on agent reliability: prompts, RAG, evals, and structured outputs that survive production, not just the demo.
Lucia Bianchi
Senior LLM Engineer
Senior LLM Engineer with 7 years of experience, specializing in multilingual retrieval and evaluation for legal technology.
Python
LangGraph
Claude API
OpenAI API+6COMMON QUESTIONS
What teams ask before they shortlist.
What does an LLM Engineer own?
Owns everything between the model API and a reliable feature: prompt design, retrieval quality, tool calling, eval datasets, regression testing, and cost-per-successful-task. The role is accountable for concrete production deliverables, including eval infrastructure, rag pipelines that answer correctly, structured outputs & tool calling. The trial scope names the output, reviewer, and acceptance evidence before work starts.
How do I know whether we need an LLM Engineer?
This role is usually the right hire when your AI feature works in the demo and embarrasses you in production; nobody can say whether last week's prompt change made things better; rAG answers are confident, cited, and wrong. On the matching call, an engineering lead checks the boundary against adjacent roles so you do not hire an impressive title for the wrong bottleneck.
How does Devlyn verify LLM Engineer skills?
Profiles show relevant work history, technical interview evidence, role-specific capabilities, and meaningful certifications where they exist. We then help you interview against your own architecture and failure cases. The final check is a paid one-week trial in your repo or approved data environment, not a generic coding puzzle.
What should the paid one-week trial produce?
The trial is scoped around committed work your team already needs. For this role, a useful first week can include built a first eval set from your real failure cases; diagnosed your worst retrieval or prompt failure; shipped one measured improvement to a live path; reported cost, latency, and quality as numbers. You keep the work whether or not the engagement continues.
What does it cost to hire an LLM Engineer?
Published dedicated rates start at $3,200 per month, or $20 per hour for Time & Material work. Senior rates are $4,800 per month or $30 per hour. The trial is paid at the same published rate, with no deposit or conversion fee.
What happens if the engineer is not the right fit?
You can stop after the paid trial or request a free replacement during the engagement. Work continues month to month with no long-term lock-in. NDA and IP assignment are completed before onboarding, access is scoped to the work, and everything produced belongs to you.
PAIRS WELL WITH