Dedicated AI developer
A senior AI/ML engineer allocated full-time to your product, building and hardening LLM, RAG or vision features inside your codebase and roadmap.
Hire AI Developers
Hire AI developers from Nextline Creative and you get senior India-based engineers who build production AI features, LLM integrations, RAG pipelines, chatbots and computer-vision workflows, not proof-of-concept notebooks that never reach users. We build for companies across the US, Canada, UK, UAE and Australia at 40 to 60 percent below local rates, with four to eight hours of daily timezone overlap and full ownership of your code, models and IP from day one. One accountable studio handles the AI layer and the application around it, so a smart feature actually ships inside a real product.
The hard part of AI work is not calling an API, it is making the result reliable, affordable and safe enough to put in front of real users, and that is where you actually need engineers. When you hire AI developers from us you get people who think about retrieval quality, prompt evaluation, hallucination guardrails, token cost and latency, not just a flashy first demo. We have shipped LLM features into our own products, including a production chatbot, so we know the gap between a notebook that works once and a feature that works for the ten-thousandth user, and we build for the second one.
Most business AI value today comes from grounding a model in your own data, so retrieval-augmented generation is core to what we do. We build RAG pipelines that chunk and embed your documents, store them in a vector database, and retrieve the right context so the model answers from your knowledge rather than making things up. We integrate the major hosted models from OpenAI, Anthropic and Google, and we use open-source models where privacy, cost or control call for it, including on your own infrastructure. Beyond text, we build computer-vision features, OCR and document extraction, image classification and detection, wiring them into real workflows rather than leaving them as experiments.
Crucially, our AI developers are also product engineers, so the model is only ever half the job. We build the application around it, the API, the data pipeline, the UI, the auth and the monitoring, so an AI feature ships as a working part of your product rather than a script someone runs by hand. We instrument cost and latency, add fallbacks for when a provider is slow or down, and evaluate output quality so you can tell whether a prompt change actually helped. It all runs under an NDA and IP assignment, so your prompts, your fine-tuned weights and your data pipelines are yours.
Pick the model that fits your roadmap - scale the team up or down as your needs change.
A senior AI/ML engineer allocated full-time to your product, building and hardening LLM, RAG or vision features inside your codebase and roadmap.
A small team of AI plus product engineers that takes a defined AI feature from prototype to production on a milestone plan, priced for the project.
A named AI developer for a set weekly block, ideal for adding an AI feature to an existing product or de-risking an approach before scaling up.
Indicative USD ranges. Every engagement is scoped and quoted for your specific needs - no hidden fees.
Scoping call: we dig into the problem, your data, whether an LLM, RAG or vision approach fits, and the realistic accuracy, cost and latency you need, then recommend an approach.
Match and prototype: you interview the proposed AI developers, and we can start with a small paid prototype that proves the approach works on your real data before a full build.
Onboarding: NDA and contract signed, access set up to your repo and to your own model-provider and cloud accounts, and evaluation criteria agreed so quality is measurable.
Build and harden: we ship the feature with guardrails, cost and latency monitoring and fallbacks, then iterate on quality and scale the team up or down monthly.
We are model-agnostic and choose based on your needs rather than a single vendor relationship. We integrate the major hosted models from OpenAI, Anthropic and Google when they give the best quality for the task, and we use open-source models, including self-hosted ones, when privacy, cost control or the ability to run offline matters more. On the scoping call we are honest about the trade-offs, because the right model for a customer-facing chatbot is often not the right one for internal document processing, and locking into the wrong provider gets expensive.
Retrieval-augmented generation means giving the model your own documents or data as context at the moment it answers, so it responds from your knowledge instead of guessing from its training. You need it whenever you want an AI feature to answer accurately about your products, policies, documents or records, which is most real business use cases. We build the full pipeline, chunking and embedding your content, storing it in a vector database, and retrieving the right passages per query, which is usually cheaper, faster to update and more accurate than fine-tuning a model on the same information.
There is no magic switch, but there is real engineering. We ground answers in retrieved data so the model has facts to work from, constrain outputs with careful prompting and structured formats, add guardrails and validation on the output, and design the UX to cite sources or fall back gracefully when confidence is low. Just as importantly, we set up evaluation so quality is measured rather than guessed, which means when we change a prompt or a retrieval step we can tell whether it actually improved things. Reliability comes from that loop, not from one clever prompt.
You own all of it. Under an NDA and IP-assignment contract from day one, the code, the prompts, the data pipelines and any fine-tuned model weights we produce are assigned to you and live in your repositories and accounts. Model API calls run through your own provider accounts, so usage and data handling stay under your control and your terms. We treat your data as confidential and design the system around whatever privacy and residency requirements you have, rather than routing it through anything of ours.
Cost is a first-class design concern, because a naive AI feature can quietly become very expensive at scale. We instrument token usage and per-request cost, cache and reuse results where it is safe, size the model to the task rather than defaulting to the largest one, and use retrieval to keep prompts small instead of stuffing in everything. Because model calls bill to your own provider accounts, you see the real numbers directly, and we tune the system against the cost and latency targets we agree on the scoping call.
Yes, and that is a common way to begin. Rather than a big rebuild, we scope a single well-defined AI feature, such as a support chatbot grounded in your docs or automated document extraction, and often start with a small paid prototype on your real data to prove it works before committing to a full build. Because our AI developers are also product engineers, they integrate the feature into your existing codebase and workflow, and you can then scale the engagement up for more AI work or down to maintenance on a monthly basis.
Tell us your stack, timezone, and roadmap. We'll share matched profiles, rates, and a start date - serving teams across the US, UK, UAE, Canada, and Australia.