Hire LLM Developers
in India
Expert LLM developers ready in 48 hours. Build custom LLM applications, production RAG systems, fine-tuned models, and AI agents using OpenAI, Claude, Mistral, LangChain, and LlamaIndex — at 60% less than US/UK rates.
What Our Hire LLM Developerss Build for You
Custom LLM Applications
Full-stack LLM products — streaming chat APIs, document Q&A, knowledge assistants, and AI-powered search — built for production reliability and scale.
RAG Systems
Production-grade Retrieval Augmented Generation pipelines over your documents, databases, and internal knowledge — with chunking, embedding, hybrid search, and re-ranking.
AI Agents & Tool Use
Multi-step AI agents using function calling, tool use, and ReAct patterns — agents that reason, search, query databases, and complete complex tasks autonomously.
Fine-Tuned Models
Dataset curation, supervised fine-tuning, RLHF, and LoRA/QLoRA techniques to adapt foundation models to your domain, style, or specialised task.
LLM Evaluation Frameworks
Systematic evaluation pipelines — automated benchmarks, human eval protocols, and LLM-as-a-judge approaches to measure and improve model output quality.
Multi-Agent Systems
Orchestrated multi-agent workflows where specialised agents collaborate — planning agents, execution agents, review agents — for complex autonomous tasks.
Prompt Engineering
Systematic prompt design, few-shot examples, chain-of-thought, structured output with JSON mode, and prompt versioning for reproducible, high-quality outputs.
LLM Observability
Instrument your LLM applications with LangSmith, Langfuse, or Arize — trace every call, monitor quality, detect regressions, and control costs.
LLM Cost Optimisation
Model routing (GPT-4 vs GPT-4o-mini), prompt caching, semantic caching, and batch processing to reduce LLM API spend by 40–70%.
Technologies & Frameworks We Cover
Why Hire Through TechTeamsOnline?
Production AI Experience
Our engineers have shipped real AI features in production — not just demos. They handle latency, cost, quality, and edge cases.
48-Hour Matching
Receive 2–3 pre-vetted profiles in 48 hours after sharing your requirements.
7-Day Risk-Free Trial
Work with your engineer for a full week. Not the right fit? Pay nothing.
60% Cost Savings
Hire senior AI engineers at $2,000–$5,000/month vs $150,000+/year in the US.
Deep Domain Expertise
Specialists who focus exclusively on this tech area — not generalists wearing an AI hat.
Free Replacement
If your engineer underperforms or leaves, we replace within 7 days at no cost.
How We Vet These Engineers
Portfolio Screen
We review shipped production systems, GitHub projects, and real business impact.
Technical Assessment
Hands-on coding challenge specific to the role — model building, pipeline design, or system architecture.
Systems Interview
Senior engineer conducts a technical design and troubleshooting interview.
Communication Fit
English proficiency and remote collaboration style evaluated.
What Clients Say
"Our LLM developer built a RAG system over 200,000 legal documents. Accuracy is 96% and our lawyers can get answers in seconds instead of hours."
"The multi-agent system for our sales team drafts personalised outreach for 500 prospects per day. Our SDR team now focuses on closing, not writing."
"Fine-tuned model we built now produces product copy in our brand voice with 95% acceptance rate. Zero manual editing required."
Frequently Asked Questions
What is an LLM developer?
An LLM developer specialises in building applications and systems powered by Large Language Models. They work with LLM APIs (OpenAI, Anthropic, Google), build RAG pipelines, create AI agents, implement fine-tuning, and ensure LLM features are production-ready with proper evaluation and monitoring.
What is the difference between an LLM developer and a prompt engineer?
A prompt engineer focuses specifically on designing and optimising prompts. An LLM developer is a full engineer — they design the system architecture, build the RAG pipeline, integrate vector databases, handle streaming, implement caching, write tests, and ship production code. Prompt engineering is one skill within that.
When should we use RAG vs fine-tuning?
Use RAG when you need to ground responses in specific, dynamic, or large bodies of private knowledge. Use fine-tuning when you need to change the model's output style, tone, or specialised reasoning — and have a large, high-quality dataset. Most production use cases benefit most from RAG first, fine-tuning second.
Can your LLM developers build multi-agent systems?
Yes. Our developers build multi-agent architectures where specialised agents collaborate — planner, executor, critic, memory — using LangGraph, CrewAI, or custom orchestration frameworks.
What vector databases do your LLM developers work with?
Our LLM developers work with Pinecone, Weaviate, Qdrant, Chroma, pgvector, and Milvus — selecting based on your scale, latency requirements, and hosting preferences.
How do you evaluate LLM output quality?
Our developers implement automated evaluation pipelines using LLM-as-a-judge (GPT-4 evaluating outputs), human annotation workflows, regression benchmarks, and tools like LangSmith or Ragas for RAG evaluation.
Ready to Hire a Senior LLM Developer?
Get 2–3 pre-vetted LLM developer profiles in 48 hours. Start with a 7-day risk-free trial.