Hire RAG & LLM application engineers
who build the pipeline, not just the prompt.
A dedicated specialist, or a pod, who treats retrieval as the engineering problem it actually is: what gets indexed, how it's chunked, what's retrieved, and whether the answer can be traced back to a real source. Vetted on real pipelines before they ever touch yours, working inside your data stack and stand-ups, and scaling up or down monthly without a hiring cycle. You question the engineer who'd build your pipeline directly, before any contract is signed.
Interview before signing · First working session inside 14 days · Scale monthly
Tell us about the role
Start Your Enquiry
Send a short brief; an itemised proposal follows within 48 hours.
Hiring a RAG and LLM application engineer through PixelCrayons gets you a vetted retrieval specialist inside your team within 14 days. They build the retrieval layer underneath an assistant or search experience: vector databases and chunking strategy, hybrid search, grounding and citation, evaluation against a ground-truth set you define, in your stack, with a named project manager and a weekly review in Prism behind them. You interview the engineer who would build your retrieval layer first, resize the engagement monthly, and avoid a full recruiting cycle and the risk it carries. 21 yrs of delivery stand behind the bench.
Judge the record,
not the adjectives.
Outcomes tied to real engagements, not averages.
Rates for brands and companies buying for themselves.
Retrieval skills,
wired to grounded answers.
Not someone who swaps in a bigger model when answers disappoint. A specialist whose job is the pipeline underneath: what's indexed, how it's chunked, what gets retrieved, and whether the answer can be checked against a source.
Retrieval systems
- Vector databases and embeddings: index design, model selection, migration
- Chunking and indexing strategy: passages that stay meaningful on their own
- Hybrid search: combining semantic retrieval with keyword and metadata filters
- Reranking: narrowing a broad retrieval set down to what actually answers the question
- Multi-source indexing: documents, tickets and structured data in one retrieval layer
LLM application architecture
- RAG pipeline design: from query to retrieval to grounded generation
- Source citation and grounding: answers that trace back to a specific passage, not a paraphrase
- Evaluation of answer accuracy against a ground-truth set your team defines
- Refusal handling: a defined behaviour for when retrieval comes back empty or contradictory
- Handoff from prompt design: building the pipeline the prompts run inside, not the prompts themselves
The commercial layer
- Working from your existing documentation and data sources as the knowledge base
- Keeping the index current as source documents change, on a schedule you set
- Cost and latency engineering: retrieval and generation sized to real usage, not guesswork
- Documentation that survives a handover: pipeline, prompts and evaluation set, not tribal knowledge
- Forecasting without invented precision
What a RAG engineer
tunes long after the demo works.
The practical side of the role: the weekly work, the interview, the data you need ready, and the hand-offs.
The loop after launch
Once the first version works, the week becomes a measurement loop. They review queries from real users, especially ones where the answer was wrong, vague or refused. Each failure gets traced: was the right passage never indexed, split badly, ranked too low, or retrieved but ignored by the model? The fix depends on the answer, so they adjust chunking, metadata filters, reranking or the index refresh schedule, then rerun the evaluation set to confirm nothing else got worse. They also watch cost and latency per query, and add each new failure case to the evaluation set.
Interview questions that reveal depth
Give them a sample of your real documents and ask how they would chunk them. A strong engineer asks about document structure, tables, headings and how often content changes before answering. Ask how they would know if retrieval got worse after a change; good answers describe a fixed evaluation set, retrieval measured separately from generation, and a human-checked sample of answers. Ask how they handle permissions, so users only see content they're allowed to see. Be cautious of candidates who talk mostly about model choice, or who have never measured retrieval apart from the final answer.
Data and decisions to settle first
List every source the system should draw from, with an owner for each: wikis, shared drives, ticket history, policy documents, product databases. Decide which source wins when two disagree. Export or connect a representative sample early, because real formatting problems only show up with real files. Write down the questions users actually ask, ideally pulled from support tickets or search logs, and have a subject expert mark the correct answers. Confirm which content is restricted and where access rules are stored. And agree which model providers and cloud regions your security team will accept.
How the role hands off
A RAG engineer owns retrieval, indexing and evaluation, but depends on others to get it right. Prompt engineers shape how the model uses what is retrieved, and the two should review failures together, since a bad answer can start on either side. Data engineers or system owners keep the source feeds reliable. Front-end or chatbot developers build the interface and decide how citations appear. Subject experts review evaluation answers and settle disputes about what is correct. Whatever the team shape, make sure someone outside engineering owns the question of what counts as a right answer.
Brief to embedded,
in two weeks.
Brief & shortlist
You describe the data sources, the stack and the gap; we propose the specialist, or pod, whose actual delivery history fits it. No generic CVs.
Interview them
You meet the engineer who'd actually design the pipeline, not an account manager relaying it secondhand. Ask them to walk through how they'd chunk your specific data; if the fit isn't right, we propose again.
Inside your knowledge base
Data sources connected, chunking and indexing reviewed, your stand-ups joined. The first working session on your retrieval pipeline happens within fourteen days of the NDA; you don't wait out a quarter of onboarding.
Scale either way
Add a second specialist when scope grows: more sources, more surfaces, an evaluation build-out; step down when it doesn't. The engagement resizes month by month, adding pipeline capacity without a headcount decision.
Requests, approvals and the weekly review for this engagement live in your Prism workspace. Each decision is recorded against the outcome it expected. See how Prism runs an engagement →
Meet the actual people before anything is signed
The engineer,
plus the evaluation discipline.
Hiring a specialist on their own gets you their pipeline skills. Hiring through a delivery organisation adds the evaluation rigor and cover that keeps a retrieval system grounded after launch, not just at the demo.
Vetted on real pipelines, not puzzles
Every specialist on the bench has built and evaluated retrieval pipelines before yours. That work ran under our own delivery standards, reviewed weekly, held to the same evidence bar you'll see. A resume can claim RAG experience; we'd rather see an evaluation set whose citation accuracy they checked themselves.
Continuity when a pipeline is mid-build
Behind the engineer: a named project manager, an escalation path and a named second RAG specialist. Briefed on your pipeline from day one, following the indexing and evaluation work as it happens. If the first specialist is out, or leaves, the second steps in without a ramp-up: the things a lone freelancer can't offer and an in-house hire needs a whole team to cover. You get an engineer and the delivery structure behind them.
Team integration, not a portal
Your Slack, your stand-ups, your repositories. Your existing vector database or cloud accounts, if you have them already. Dedicated means building inside your repos and routines, not retrieval tickets passed to someone else's queue.
Evidence over confident-sounding answers
Grounding and citation aren't a feature to bolt on later. They're how the pipeline is built from the first index. Every claim about accuracy is checked against an evaluation set, not asserted, and when the honest answer is 'the sources don't support this,' that's what ships.
How you typically hire,
versus through us.
A full-time in-house hire earns its keep once retrieval is a permanent, central function for you, and we'll tell you plainly when it is. This page covers every other case.
| Hiring it yourself | Through PixelCrayons | |
|---|---|---|
| Time to a working specialist | A full recruiting cycle: sourcing, interviews, notice periods, onboarding | Inside 14 days of a signed NDA, interview included |
| Vetting | CV screening and interview performance: retrieval quality only shows up once real queries hit it | Delivery history on real retrieval pipelines under our own standards, reviewed weekly |
| Management overhead | Yours entirely: objectives, review, career development, leave cover | A named PM and escalation path included; you review the evaluation results, not manage headcount |
| Scaling | A full hiring cycle in either direction: months to add a second specialist, an awkward exit to remove one | Resize monthly: add a specialist when the evaluation build-out grows, step down when it doesn't |
| Risk when it doesn't work | A mis-hire's flawed retrieval design sits unnoticed until users stop trusting the answers | Propose-again is built in; the index design and evaluation results transfer with the handover |
Structure a retrieval pipeline can actually work with.
A search-visibility engagement, shown here because it is the discipline retrieval lives or dies by. A B2B SaaS site's ambiguous naming and unstructured pages were defeating search engines the same way they'd defeat a retrieval pipeline; the fix was entity cleanup, schema and answer-first structure, and organic traffic rose in the months that followed, with AI engines citing the pages. A document a machine can parse and quote is a document a knowledge system can retrieve and cite: the same structural work this role starts from.
Read the case study →Frequently
asked.
A written proposal with roles, rates and availability arrives within 48 hours of the brief and the shortlist follows within days; you interview the specialist the same week (a real retrieval pipeline they've built is available to walk through under NDA on request), and the first working session inside your knowledge base happens within 14 days of a signed NDA. If your documentation is scattered across several systems (most is), expect the first fortnight to prioritise source mapping and permissions before indexing starts in earnest.
A prompt engineer designs and evaluates the instructions a model runs on: wording, few-shot examples, output format. This role builds what those prompts run against: the retrieval pipeline, the vector database, the chunking strategy, the evaluation harness that checks whether retrieved passages actually support the answer. In practice most RAG builds need both, and they hand off constantly, since the prompt only works because the retrieval underneath it returns the right passages. If your gap is specifically prompt quality on an existing pipeline, ask about the prompt-engineering role instead; if the gap is the pipeline itself, this is it.
Any pipeline with a language model in it can; anyone promising zero is overselling. What good engineering changes is the failure mode: generation restricted to retrieved passages, citations on every answer so a human can check them, and an explicit refusal path for when retrieval comes back empty or the sources disagree. Before anything ships, it runs against an evaluation set built from real questions and checked against ground truth you define, including the refusal cases, not just the successful ones.
Usually yes, and the audit will say where not. Messy, real documentation, overlapping wikis, outdated policies, tickets that contradict the manual, is the normal starting point; part of the work is triage, deciding what's authoritative and flagging what contradicts itself rather than quietly averaging it. What retrieval can't fix is knowledge nobody wrote down. Where the audit finds those gaps, you get a specific list of the questions people ask that no document answers.
If the goal is answers grounded in your own documents, hire for the retrieval pipeline: what's indexed, how it's chunked, what gets retrieved, and whether each answer can be checked against a source. That is this role. A developer who swaps in a bigger model when answers disappoint won't fix a retrieval problem.
Tell us early and we'll put forward another retrieval engineer; that's part of the model, not something you have to argue for. Whoever replaces them inherits the index design, chunking decisions and evaluation results the first specialist logged, so the switch costs days, not a restart. And if what you actually need turns out to be a scoped pilot rather than an embedded engineer, we'll say so and route you there instead. The proposal names the second retrieval engineer and says whether their cover is included in the monthly rate.
Interview the person,
not the pitch deck.
Brief us on the data sources and the gap, get a written proposal within 48 hours and a shortlist within days, and meet the actual specialist before anything is signed. If what you really need turns out to be a scoped pilot rather than a person, we'll say so on the first call.
Proposal in 48 hours · Interview before signing · Scale monthly
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