Hire Dedicated RAG / AI Pipeline Engineers — India, US, UK & Australia
Hire pre-vetted RAG engineers who build retrieval-augmented generation pipelines that give LLMs accurate, grounded answers from your own data. Golonex places dedicated India-based AI engineers with US, UK, Australian, and Indian teams — working in your timezone, under NDA, integrated within one week and with no lock-in.
What a RAG / AI Pipeline Engineer does
A RAG / AI pipeline engineer designs the full retrieval stack behind LLM applications: document chunking, embedding generation, vector storage and retrieval, re-ranking, prompt orchestration, and evaluation. Our engineers work fluently across LangChain, LlamaIndex, and custom pipelines, wiring models like GPT, Claude, and open-weight LLMs to your knowledge base with guardrails, caching, and observability from day one.
Core expertise
What a Golonex RAG / AI Pipeline Engineer builds for you
How it works
Engagement models
Based in India, working in your timezone. Onsite options available across the US, UK, and India.
Category
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Data, AI & ML Engineers
The Golonex difference
Named, pre-vetted engineers
Every RAG / AI Pipeline Engineer is screened for technical depth, communication, and reliability before you meet them — a sub-30% pass rate at the technical stage.
Integrated within 1 week
From enquiry to working team member in 5 business days. We handle onboarding, NDA, and access logistics.
Your timezone
India-based talent working overlapping hours with US, UK, and Australian teams. Daily standups and real-time collaboration, agreed upfront.
NDA-protected, no lock-in
Full IP and confidentiality protection as standard. Month-to-month engagements — scale up or down as your project evolves.
Hiring a RAG / AI Pipeline Engineer — FAQs
How quickly can I hire a RAG engineer through Golonex? +
Usually within one week. We shortlist pre-vetted engineers matched to your stack — LangChain, LlamaIndex, or a custom retrieval pipeline — you interview them, and the selected engineer is onboarded to your tools and repos within 5 business days.
Do your RAG engineers work in my timezone? +
Yes. Our engineers are based in India but work overlapping hours with US, UK, and Australian teams. The overlap window is agreed before the engagement starts so standups, prompt reviews, and pairing all happen in real time.
Which LLMs and vector databases do they work with? +
Our engineers work across OpenAI, Anthropic, and open-weight models, and with vector stores including Pinecone, Weaviate, Qdrant, and pgvector. They pick the retrieval and embedding strategy that fits your data and latency needs rather than forcing one stack.
Can they reduce hallucinations in an existing LLM app? +
Yes. A common engagement is improving an existing RAG system — better chunking, hybrid retrieval, re-ranking, and prompt grounding — then measuring the gains with evaluation frameworks like RAGAS so quality improvements are demonstrable, not guessed at.
How do you vet RAG engineers? +
Every candidate passes a technical assessment on real retrieval and LLM-orchestration scenarios, a communication and remote-working assessment, and reference checks from at least two prior engagements. Our technical pass rate is under 30 percent before you meet anyone.
Ready to hire a RAG / AI Pipeline Engineer?
Tell us the role, stack, and timeline. We'll match you with a named, pre-vetted engineer — integrated within 1 week.