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Dedicated AI / LLM engineers

Hire AI and LLM developers who will put a model behind a product, not a demo.

Need an engineer for RAG, LangChain, or an OpenAI feature inside a Laravel or Node app? DayDecoder hires AI people who care about cost caps, citations and a human review step. Overlap hours for the US, UK, Europe and Australia. No invented accuracy percentages.

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What our AI developers build into products.

We add models to software you already run. We do not sell a chatbot that cannot see your data, and we do not promise a model will “replace your team”.

Public AI-shaped work on this site includes the WhatsApp marketing SaaS (OpenAI-assisted replies and chatbot flows), an AI SaaS platform for content workflows, an AI customer support assistant (summaries, suggested replies, knowledge search), document intelligence with OCR, and an AI SEO planning tool. Those products list OpenAI, Laravel, and sometimes React, Vue or Python. That is the pattern: a model call is a feature, not the company.

Our blog is the same story in long form: how to integrate LLMs into Laravel or Node, RAG explained without a research paper, chatbot cost drivers in 2026, and ten SMB automations with illustrative ROI sketches rather than fabricated client results. A hire on this page is expected to follow those constraints — tenant filters on retrieval, cost caps, and a review queue where a wrong field would hurt.

  • RAG with citations

    Chunking, embeddings, retrieval, and answers that can point at a source. Tenant filters so one customer never sees another customer’s files.

  • LangChain (or plain SDK) pipelines

    LangChain when the chain is real. Direct OpenAI or Claude SDKs when a chain would only hide a for-loop. We pick the boring option.

  • Assistive UI, not autopilot

    Suggested replies, summaries, extraction with a human confirm. The support-assistant and document-intelligence projects are this shape.

  • Cost and evals

    Token budgets, caching, and a handful of golden questions you re-run after a prompt change. No “the model is smart now” as a release note.

Skills and tool stack

AI / LLM stack we staff.

The founder’s published tools include OpenAI, Claude, LangChain and LlamaIndex. Client work uses what the product already standardised unless we agree a change.

Typical path: Laravel or Node owns auth and billing; a worker calls the model; a vector store holds embeddings if RAG is in scope. Python shows up for OCR and heavier pipelines. We will not stand up a new vector database because a tweet named one. We will not fine-tune as a first move when retrieval would do.

Models

  • OpenAI
  • Claude
  • others you already have keys for

Orchestration

  • LangChain
  • LlamaIndex
  • plain SDK + queues

RAG

  • embeddings
  • pgvector or a hosted store
  • citation-friendly chunking

Product

  • Laravel / Node / Python workers
  • review queues
  • token budgets

Engagement models

Dedicated AI capacity, part-time LLM work, or hourly spikes.

A RAG feature that has to stay correct usually wants the same engineer every week. A prompt incident can be hourly.

Dedicated full-time AI/LLM developer

Owns retrieval, prompts, evals and the worker that calls the model. Best when AI is in the product roadmap, not a hackathon leftover.

Part-time dedicated

A few days a week beside your Laravel or Python team. Typical for a support copilot or an extraction lane that is real but not a full headcount.

Hourly AI help

A runaway bill, a leaking prompt, a RAG quality dive. Time-logged. Training a foundation model is not an hourly task and we will not pretend it is.

Hiring process

How we hire an AI developer onto your product.

Bring the data you are allowed to use, the cost you can live with, and the thing the model is not allowed to get wrong.

  1. 1

    Name the failure

    Thirty minutes with the founder. If a wrong answer would move money or medical data, the design changes. We would rather refuse than bluff.

  2. 2

    Written AI proposal

    Dedicated, part-time or hourly, plus whether RAG, a simple prompt, or no model at all is the honest first step.

  3. 3

    Data and keys in your account

    API keys stay yours. Documents stay in your bucket. We do not train on your corpus for other clients.

  4. 4

    First evaluated slice

    A golden set, a worker, a UI with a review step. Proof before a homepage chatbot.

  5. 5

    Weekly demo of behaviour

    The same questions, better or not. Cost for the week. In your overlap hours.

AI delivery for US, UK, EU and Australian product teams.

Prompt and retrieval changes need overlap because they are product decisions, not just deploys. We keep afternoon windows for the UK and Europe and a late slot for US review of staging answers. Batch embedding jobs run in India hours so your morning has an index, not a progress bar.

Model providers fail in their own regions. We document fallbacks. We will not promise five-nines on someone else’s API. If you need a human-only path when the model is down, that is part of the proposal.

  • United States

    Morning updates waiting in your inbox when the US workday starts.

  • United Kingdom

    Same-day overlap for calls, reviews, and decisions.

  • Europe

    Afternoon overlap with Central and Eastern European teams.

  • Australia

    Early-morning calls that fit the Australian workday.

Why hire AI developers from DayDecoder.

You get LLM features from a team that already ships Laravel, Python and WhatsApp products — plus public writing that refuses fake case studies.

  • Product engineers, not slide-deck ML

    The hire can ship the worker, the UI, and the review queue. A model call without a home in the app is not done.

  • RAG with tenancy and citations

    The same rules as our RAG article. If we cannot isolate tenants, we will not put their files in one soup.

  • Cost talked about in week one

    The chatbot-cost guide is indicative, not a quote. Your proposal will still mention token budgets because surprise bills kill AI features.

  • You own prompts and pipelines

    Custom code, prompts and eval sets transfer on payment. Your documents never become our training set.

Relevant work

AI work already on this site.

Products that list OpenAI or OCR in the stack, plus the AI integration service page. Client names stay confidential.

Marketing & messaging Web (SaaS)

WhatsApp Marketing, Team Inbox & Automation SaaS

A multi-tenant SaaS that lets businesses run WhatsApp marketing and support from one console: template campaigns, a shared team inbox, contact management, chatbot flows and AI-assisted replies, sold on subscription plans.

Laravel MySQL WhatsApp Business API OpenAI
Read case study : WhatsApp Marketing, Team Inbox & Automation SaaS

Related reading from our blog.

All articles

Related services and hires.

Services

Related hires

FAQ

Questions about hiring AI/LLM developers.

Straight answers on the stack, ownership and time zones. Email us if yours is missing.

contact@daydecoder.com

Yes. That is a standard brief: chunk, embed, retrieve, cite, filter by tenant. We will not skip the isolation step to ship a prettier demo.

Let's talk

Need an AI/LLM developer on your product?

Tell us what you are building. You will get honest advice on whether you need a dedicated hire, part-time capacity, or a scoped build.

  1. 1

    We reply within 24 hours

    A real person reads your message and replies on the next business day at the latest.

  2. 2

    Free 30-minute call

    We talk through your goals, users, and budget, and answer every question.

  3. 3

    Proposal & fixed quote

    You get a written scope, timeline, and price. No obligation.

contact@daydecoder.com Ahmedabad, India

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