Parvessh Agarwal
Founder & CEO
An entrepreneur at heart. He links business analysis, strategy, clear goals and ROI to practical technology.
LinkedIn →Hire RAG & LLM application engineers
who tie answers to your documents.
PixelCrayons is your point of contact for hiring a RAG and LLM application engineer. An LLM (large language model) is the AI that writes answers, like ChatGPT. RAG (retrieval-augmented generation) means it first looks up the right passages in your own documents. We scope the role and write the proposal. We then find and supply the specialist, or a small team. They treat that lookup step as the real engineering problem. They decide what gets indexed for search, how documents are split, and what is found. They also check that each answer traces to a real source. Each engineer is vetted on real systems before they touch yours. They work inside your data tools and stand-ups, and you scale up or down monthly without a hiring cycle. You question the engineer who would build your system directly, before any contract is signed.
Interview before signing · One contact throughout · Scale monthly
Tell us about the role
Start Your Enquiry
Send a short brief. Once we have a complete brief, we confirm the date you will receive your itemised proposal.
What you get
PixelCrayons is your point of contact for hiring a RAG and LLM engineer. We source and deliver the specialist. They build the search step behind AI answers, from vector databases to source citations. They test results against correct answers you define. You interview the engineer first. PixelCrayons stays your account contact for escalation, billing and status.
- Vetted on real RAG systemsEvery specialist has built and tested search systems for AI, vetted against our delivery standards.
- One point of contact for everythingPixelCrayons handles scope, escalation, billing and status, and answers for every commitment.
- Working inside your teamYour Slack, stand-ups and code repositories, plus your existing vector database or cloud accounts.
Our work
See what we've built
and the results it brought.
Real projects for real clients, with results we can show.
2004
Established
500+
Agencies served
4,500+
Projects delivered
340%
Revenue growth · 7 months
eCommerce
+127%
Organic traffic · 5 months
SaaS
85%
Faster delivery · backlog cleared
via agency partner
Technologies we work with
- Commerce and web
- Shopify
- WooCommerce
- Magento
- WordPress
- Webflow
- Next.js
- Marketing
- Google Ads
- Meta Ads
- GA4
- Klaviyo
- Cloud
- AWS
- Microsoft Azure
- Google Cloud
- AI models
- OpenAI
- Anthropic
- Google Gemini
- Build and deploy
- Docker
- Kubernetes
- Terraform
- GitHub Actions
- CRM and automation
- HubSpot
- Salesforce
- Zapier
- n8n
Product names and marks are trademarks of their owners. Naming a technology means our teams build with it. It does not mean a partnership, certification or endorsement.
What they cover
RAG engineering skills
for AI answers you can check.
This is not someone who swaps in a bigger model when answers disappoint. Their job is the system underneath. That means what's indexed, how it's split, what gets found, and whether each answer can be checked against a source.
Search and retrieval
- Vector databases and embeddings (number codes that capture meaning): index design, model choice and moves between systems
- Splitting documents into chunks that still make sense on their own
- Hybrid search: meaning-based search combined with keyword and metadata filters
- Reranking: narrowing a broad set of results to what actually answers the question
- One search index across documents, tickets and structured data
Building the AI answer system
- RAG system design: from the question, to the search, to an answer based on what was found
- Source citations: answers that trace back to a specific passage, not a paraphrase
- Checking answer accuracy against a set of correct answers your team defines
- Clear behaviour when the search finds nothing or the sources disagree
- Building the system the prompts run inside, while prompt design stays a separate job
Upkeep, cost and handover
- Using your existing documents and data sources as the knowledge base
- Keeping the index up to date as documents change, on a schedule you set
- Cost and speed sized to real usage, not guesswork
- Notes that survive a handover: the system, prompts and test set, not knowledge in one head
- Forecasting without invented precision
In practice
What a RAG engineer
keeps tuning after the demo works.
The weekly work, the interview, the data to have ready, and who they work with.
The weekly loop after launch
Once the first version works, each week becomes a cycle of measuring and fixing. They review questions from real users, especially ones where the answer was wrong, vague or refused. Each failure gets traced. Was the right passage never indexed, or split badly? Was it ranked too low, or found but ignored by the model? The fix depends on the cause. They adjust chunking, metadata filters, reranking or the index refresh schedule. Then they rerun the test set to confirm nothing else got worse. They also watch cost and speed per question, and add each new failure to the test set.
Interview questions that show real experience
Give them a sample of your real documents and ask how they would split them. A strong engineer first asks about document structure, tables, headings and how often content changes. Ask how they would know if search got worse after a change. Good answers describe a fixed test set and search measured apart from the final answer. They also mention a sample of answers checked by a person. Ask how they handle permissions, so users only see content they're allowed to see. Be careful with candidates who talk mostly about model choice. The same goes for anyone who has never measured search apart from the final answer.
Data and decisions to settle first
List every source the system should draw from, with an owner for each. That could be wikis, shared drives, ticket history, policy documents and product databases. Decide which source wins when two disagree. Share a real sample early, because formatting problems only show up with real files. Write down the questions users actually ask, ideally from support tickets or search logs. Have a subject expert mark the correct answers. Confirm which content is restricted and where access rules are stored. And agree which AI providers and cloud regions your security team will accept.
Who they work with
A RAG engineer owns search, indexing and testing, but depends on others to get it right. Prompt engineers shape how the model uses what is found. 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 test answers and settle disputes about what is correct. Whatever the team looks like, someone outside engineering should own what counts as a right answer.
How it works
How hiring works,
with one contact throughout.
Start
Brief and a call
We sign an NDA, and you describe the data sources, your tech stack and the gap to PixelCrayons.
Once the brief is complete
Proposal and interview
We confirm when the proposal will arrive. We put forward the specialist whose track record fits. You interview them, including how they'd split your data, before anything is signed.
Your decision
You sign off
You confirm the scope, the payment and the access we need. Nothing starts before this.
Typically 2 to 3 business days later
The work starts
They connect your data sources and review how documents are split and indexed. Then they join your stand-ups and start on your search system.
Every month
Scale up or down
Add a second specialist when scope grows: more sources, more channels or a bigger test set. Step down when it doesn't. You resize monthly through the same PixelCrayons account team.
You make requests, give approvals and see the weekly review in your Prism workspace. Each decision is written down with the result it was meant to bring. See how Prism runs the work →
Get a Proposal
Meet the people who will do the work before you sign
Why through us
Why hire through us:
the engineer and the testing behind them.
PixelCrayons scopes the RAG hire, supplies the engineer and manages your account. You get one contact and a well-matched specialist. They keep answers tied to sources after launch, not just in the demo.
Vetted on real RAG systems
Every specialist we propose has built and tested search systems for AI before yours. They are vetted against our own delivery standards. A CV can claim RAG experience. We would rather see a test set whose citation accuracy they checked themselves.
One point of contact for everything
PixelCrayons scopes the RAG role and writes the proposal. As your one account team, we then handle escalation, billing and status. Our engineer builds the system. You sign with PixelCrayons, and we answer for every commitment in the proposal for as long as the work runs.
Working inside your team
Your Slack, your stand-ups, your code repositories. Your existing vector database or cloud accounts, if you already have them. Dedicated means building inside your code and routines. Search tasks never go to someone else's queue.
Answers backed by sources
Source checks and citations are not a feature added later. They shape the system from the first index. Every claim about accuracy is checked against a test set, not just stated. When the honest answer is 'the sources don't support this,' that is what the system says.
Side by side
Hiring on your own,
compared with hiring through us.
A full-time in-house hire is worth it once AI search is a permanent, central job for you. 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 | A shortlist and an interview before anything is signed |
| Vetting | CV screening and interview performance: search quality only shows once real questions arrive | A track record on real RAG systems, checked against our own standards |
| Management work | All yours: goals, reviews, career growth and leave cover | PixelCrayons' account team handles day-to-day contact and escalation; you review the test results, not manage staff |
| Scaling | A full hiring cycle either way: months to add a second specialist, an awkward exit to remove one | Resize monthly: add a specialist when the testing work grows, step down when it doesn't |
| Risk when it doesn't work | A poor hire's weak search design goes unnoticed until users stop trusting the answers | If the fit is wrong, we propose someone else; the index design and test results pass on with the handover |
Who is accountable
The people responsible
for your work.
Saundaraya Gupta
Technical Manager
Brings technical work, client updates and project management together, so work moves smoothly from brief to launch.
LinkedIn →Every engagement
A named coordinator and escalation contact
Know who coordinates your engagement, who owns the work and how to escalate an issue.
All engagements. Roles may be combined and vary with your engagement.
Before you sign
Our commitments
Each one says where it applies and links to its full terms.
Time-zone overlap
At least four hours a day inside your core working hours. Two hours plus a daily written handover for the US West Coast.
Managed specialists only; not a staffing promise for project teams.
Full termsClear IP & Ownership Terms
Your materials remain yours. Bespoke deliverable rights, licences and handover are agreed before work starts.
All engagements.
Full termsClear Delivery & Escalation Ownership
Know who coordinates your engagement, who owns the work and how to escalate an issue.
All engagements. Roles may be combined and vary with your engagement.
Full termsWhy not just hire a freelancer?
A freelancer can be the right choice for a single, well-defined task. We are set up for work that needs an accountable team: a named coordinator, delivery reviewed by QA and a senior lead before release, and continuity arrangements written into the engagement. Confidentiality, ownership and escalation terms are agreed in writing before work starts.
How does time-zone overlap work?
Your engineers work at least four hours a day inside your core working hours. We set the window with you before the engagement starts and hold it for as long as the engagement runs. For the US West Coast the live overlap is two hours each morning, with a written handover at the end of every Indian day. We do not run night shifts: engineers who work nights leave quickly, and keeping the same people on your team matters more to us than a longer overlap.
Can we try you before we commit?
For eligible managed specialists, yes: up to 14 calendar days or 80 logged working hours, whichever comes first. Stop within it and eligible trial hours are free; continue and they're billed at the agreed rate. Unpaid trial work stays ours. Project work and other roles get a paid pilot with scope, acceptance criteria, duration and fee agreed first.
What if we want to stop?
Cancellation, notice and any renewal terms are written into your proposal or statement of work before you accept it. If an engagement ends early, we hand over completed, paid-for work and list any unfinished work separately. For an eligible specialist trial, you can stop within the trial window and pay nothing for eligible trial hours.
How we handle claims, credentials and your data: group credentials, dated ratings, how case figures are signed off, client permissions and data handling.
How we vet
How we vet a RAG engineer
and what to ask them.
A candidate who reaches for a bigger model has not found the search problem. We vet against our own delivery standards before anyone is proposed. In your interview, these tests look at what sits underneath the answer.
| What to test | What to ask | Be careful if |
|---|---|---|
| Splitting your documents | Share a sample of your real documents, and ask how they would split them. | They answer before asking about document structure, tables, headings and how often the content changes. |
| Measuring search | How they would know search got worse after a change. | They have never measured search apart from the final answer. Good answers use a fixed test set and a sample checked by a person. |
| Tracing a wrong answer | One wrong answer from a test run, and where they would look first. | Model choice is the first suspect. A good trace asks if the passage was never indexed, split badly, ranked too low, or found and ignored. |
| Permissions | How users see only the content they are allowed to see. | No view on where access rules are stored or how search respects them. |
| Empty or conflicting sources | What the system does when search finds nothing or two sources disagree. | It answers anyway, or blends the conflict. Expect a set way to decline, and conflicts flagged. |
No trial on this role. Where documents are scattered across systems, the first fortnight goes on mapping sources and permissions before indexing starts.
Questions
Frequently
asked.
The engineer comes from our Vinove group engineering team. PixelCrayons scopes the RAG role, writes the one proposal and is your contact throughout. You contract with PixelCrayons, and we answer for every commitment on this page, for as long as the work runs.
We typically reply within four business hours. Once we have a complete brief, we confirm when your proposal will arrive. You interview the specialist before anything is signed. If your documents are scattered across several systems, expect the first fortnight to go on mapping sources and permissions. Indexing starts after that.
A prompt engineer designs and tests the instructions a model runs on: wording, examples and output format. This role builds what those prompts run against. That means the search system, vector database, document splitting and test setup. Most RAG builds need both. If your gap is prompt quality on an existing system, ask about the prompt engineering role instead.
It can, because any system with a language model can make things up. Anyone promising zero is overselling. Good engineering makes failure safer. Answers are limited to the passages found, every answer has citations, and there is a clear way to decline. That happens when search finds nothing or sources disagree. Before anything ships, it runs against a test set checked against correct answers you define, including the decline cases.
Usually yes, and the review will say where not. Overlapping wikis, outdated policies and tickets that contradict the manual are the normal starting point. Part of the work is deciding which source is right and flagging conflicts. Search can't fix knowledge nobody wrote down. So you get a list of the questions no document answers.
If you want answers based on your own documents, hire for the search system. That means what's indexed, how it's split, what gets found, 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 search problem.
Tell us early, and we'll put forward another RAG engineer. That is a normal part of the engagement. The new engineer gets the index design, splitting decisions and test results the first one logged. So the switch costs days, not a restart. If you need a scoped pilot rather than a hire, we'll point you there instead.
Interview your RAG engineer
before you sign anything.
Tell PixelCrayons about your data sources and the gap. We scope the role and stay your point of contact throughout. We deliver the specialist under one written proposal. Then you meet the actual specialist before anything is signed. If you really need a scoped pilot rather than a person, we'll say so on the first call.
Reply within four business hours (typical) · Interview before signing · Scale monthly
Last updated