Dedicated AI & ML Talent

Hire AI Developers Who Ship Production AI, Not Just Demos

Bring on a vetted AI developer, or a full dedicated team, hourly, part-time or full-time, for LLM applications, RAG pipelines, AI agents, chatbots and assistants, model integration, fine-tuning, automation and the evaluation and MLOps work that keeps AI reliable once real users depend on it.

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See Engagement Models

Production

Evals, guardrails, monitoring, not demos

3 models

Hourly, dedicated, project-based

1 wk

Typical time to onboard a developer

No lock-in

Month-to-month engagements

Our Clients

Trusted by Visionaries, Built for All

Bold startup or growing enterprise, we craft digital experiences engineered to scale with your vision.

LLM & RAG Apps
AI Agents
Chatbots & Assistants
Model Integration
Automation
Python & MLOps

Why Hire AI Developers

Developers Who Take AI From Prototype To Production

A working demo is the easy part. Our AI developers understand where the real decisions live, which problems actually need a model and which need plain code, how to ground a model in your data, how to measure whether answers are good, and how to keep latency, cost and reliability under control once real users depend on it.

Pre-Vetted AI Skills

Every developer is assessed on real AI work, LLM applications, RAG, agents, evaluation and model integration, not a generic coding puzzle.

Flexible Engagement

Scale from a single developer to a full AI team as the roadmap grows, without renegotiating a new contract each time.

Fast Onboarding

Most engagements start within a week, with a developer who can read an existing codebase and start shipping AI features from day one.

No Long-Term Lock-In

Month-to-month engagement. Scale up, scale down or wrap up the work without a fixed-term contract working against you.

Engagement Models

Three Ways To Bring An AI Developer Onto Your Team

Pick the model that fits how much AI work you actually have, not a one-size-fits-all retainer.

Hourly

For a proof of concept, a single integration, or a project whose scope is still moving.

Billed for hours actually worked
No minimum monthly commitment
Good fit for prototypes and small features

Dedicated Developer

A developer works exclusively on your product, part-time or full-time, for the length of the engagement.

Consistent developer, no context switching
Direct communication with your team
Best for ongoing AI product roadmaps

Project-Based

A fixed-scope build with a defined deliverable, timeline and price.

Clear scope agreed before work starts
Fixed cost, fixed timeline
Best for a single, well-defined build

Skills Our Developers Cover

Everything An AI Project Actually Needs

LLM Application Development
RAG & Retrieval Pipelines
AI Agents & Tool Use
Chatbots & Assistants
Prompt Engineering
Fine-Tuning & Evaluation
Vector Databases & Embeddings
Model Integration & APIs
Python, FastAPI & Data Pipelines
Automation & Workflow AI
MLOps & Deployment
Guardrails, Cost & Latency

Freelancer vs Agency vs Dedicated Team

Choosing The Right Way To Staff An AI Project

Model	Reliability	Communication	Best For
Freelance Marketplace	Variable, depends entirely on the individual	Often async, timezone dependent	Very small, one-off tasks
Traditional Agency	Consistent but slower to scale up or down	Layered through account managers	Large, fixed-scope projects
Dedicated Developer (Us)	Vetted, consistent, direct accountability	Direct with the developer and our team	Ongoing AI roadmaps and integrations

How Hiring Works

From First Call To Onboarded Developer

1Discovery Call

2Developer Match

3Trial Task

4Onboarding

5Ongoing Delivery

Day 1

Discovery Call

We look at your use case, the data behind it, your existing stack and the specific AI skills you need.

Day 2 to 4

Developer Match

We shortlist developers whose real project history matches your use case and requirements.

Day 4 to 6

Trial Task

A small paid trial task on your actual problem, so you see real working code before committing.

Week 1

Onboarding

Access, repo, data and communication channels set up so the developer can start contributing immediately.

Ongoing

Delivery

Regular check-ins and progress updates, with the flexibility to scale the engagement up or down.

How Fast Can A Developer Start?

Most engagements begin within a week of the discovery call, sometimes sooner for urgent work.

How Our AI Developers Work

Built For Production, Not Just A Prototype

The difference between a demo and a product is everything that happens after the first working response. Our AI developers ground models in your own data with retrieval instead of hoping the model just knows, they build evaluation sets so quality is measured rather than guessed, and they add guardrails, fallbacks and monitoring so failures are caught early. They keep an eye on token cost and latency from the start, because an assistant that is accurate but slow or expensive does not survive contact with real users.

Explore our AI development services

From prototype to production

Ground the model in your data with retrievalRAG

Measure answer quality with eval setsEvals

Add guardrails, cost limits and monitoringGuardrails

Tech Stack

The AI Stack Our Developers Work In Daily

From the models themselves to the tooling that keeps an AI feature grounded, measured and reliable in production.

Python
OpenAI API
Anthropic Claude
LangChain
LlamaIndex
LangGraph
Hugging Face
PyTorch
FastAPI
pgvector
Pinecone
Docker
Node.js
PostgreSQL

Why Raulji

An AI Team That Owns The Outcome

You are not renting a resume. You get a developer backed by a team that can cover the rest of the product too, from the data pipelines feeding the model to the application and interface your users actually touch.

Full-Service AI Team

Need more than one developer? We cover the whole AI services range, so extra hands are already in-house.

Agents And Automation

When the work is more than a chatbot, our AI agent development and automation team can take it further.

Shipped Into Real Products

An AI feature has to live inside an app. Our Node.js development team builds and maintains the services around it.

What The Work Looks Like

Three Typical AI Engagements

To show how a developer actually slots into an AI roadmap, here are three example scenarios that reflect the kind of work these engagements cover.

Please read these as illustrative examples, not client case studies. They describe representative AI work rather than any specific customer, and the outcomes described are not measured results. Our published, verifiable client results are eCommerce projects, and you can read those in full on our case studies page.

Illustrative example
RAG assistant

A Support Team Drowning In The Same Questions

The situation: a company has years of help articles, policies and past tickets, but staff still answer the same questions by hand because nobody can find the right document fast enough. A plain chatbot was tried and made things up, so trust in it collapsed.

How the engagement runs: a dedicated developer builds a retrieval layer over the real documents, so answers are grounded in source material with citations instead of guesses. An evaluation set of real questions measures accuracy before rollout, and low-confidence answers hand off to a human rather than inventing something.

Illustrative example
Workflow automation

A Manual Process That Eats Hours Every Week

The situation: a team spends hours reading incoming emails and documents, pulling out key fields and copying them into another system by hand. It is slow, easy to get wrong, and nobody enjoys it.

How the engagement runs: a developer builds an automation that extracts the structured fields with a model, validates them against clear rules, and routes anything uncertain to a person for a quick check. The routine cases flow through on their own, and the team reviews exceptions instead of every single item.

Illustrative example
Model integration

An App That Needs AI Features Without A Rewrite

The situation: an existing product wants AI features, summaries, drafting help, smart search, but the team has no in-house AI experience and does not want to bolt on something fragile that breaks under load.

How the engagement runs: a developer integrates the model behind clean, well-typed endpoints in the existing codebase, adds caching and rate limiting so cost stays predictable, and wraps the calls in monitoring and fallbacks so a slow or failing model degrades gracefully instead of taking a feature down.

Frequently Asked Questions

Common Questions About Hiring AI Developers

What can I hire an AI developer from Raulji Technologies for?

Our AI developers build LLM-powered applications, retrieval (RAG) systems over your own documents, AI agents and assistants, chatbots, model integrations into existing apps, and automations that replace slow manual work. They also handle the less glamorous parts that decide whether AI actually ships: evaluation, guardrails, cost and latency control, and monitoring in production.

How quickly can an AI developer start?

Most engagements begin within a week of the discovery call, and sometimes sooner for urgent work. After a short call to understand your use case and data, we shortlist matching developers, you interview the ones you like, and the selected developer joins your tools and repository so they can start contributing right away.

What engagement models do you offer for AI developers?

Three: hourly for a proof of concept or a single integration, dedicated (part-time or full-time) for an ongoing AI roadmap, and project-based for a fixed-scope build with a defined deliverable and price. You can start with one and switch as your needs change, with no long lock-in.

Do I need a large dataset to use AI in my product?

Usually not. Many valuable AI features run on top of general models with retrieval over your existing documents or database, so no model training is required. Fine-tuning or custom models only make sense for specific cases, and an honest developer will tell you when plain code, a general model with good prompts, or retrieval is the better fit.

How do you keep an AI feature from making things up?

By grounding answers in your real content with retrieval and asking the model to cite its sources, by building evaluation sets that measure accuracy on real questions before rollout, and by adding guardrails so low-confidence answers hand off to a human or a safe fallback instead of inventing something. Reliability is treated as an engineering problem, not a hope.

Which models and tools do your AI developers work with?

They work across the major model providers including OpenAI and Anthropic Claude, plus open models via Hugging Face, and use tooling such as Python, LangChain, LlamaIndex, LangGraph, FastAPI and vector stores like pgvector and Pinecone. We pick the model and stack that fit your accuracy, cost, latency and privacy needs rather than defaulting to one vendor.

Can an AI developer work inside our existing codebase?

Yes. Our developers integrate AI behind clean, well-typed endpoints in your current application, add caching and rate limiting so cost stays predictable, and wrap model calls in monitoring and fallbacks so a slow or failing model degrades gracefully. You do not need to rebuild your product to add AI to it.

Who owns the code, models and data?

You do. All code, prompts, configurations and deliverables are your property under a written agreement and an NDA signed before work begins, and developers commit directly to your repositories. Your data is used only to build and run your features, never shared with other clients.

How much does it cost to hire an AI developer?

Cost depends on the seniority, the engagement model and the complexity of the work, so we quote transparently after the discovery call with no hidden fees. Because our team is based in India, you get senior, English-fluent AI developers at rates that are typically well below equivalent US or UK in-house hires.