Hire Remote Prompt Engineers & LLM Specialists

Hire Prompt Engineers & LLM Specialists Who’ve Shipped Production AI Systems

Your AI prototype worked in a notebook. Production is a different story — hallucinations, latency spikes, retrieval failures, and prompts that degrade after 30 interactions. The prompt engineers and LLM specialists you need have already solved these problems for companies building $10M+ AI products.

We match you with senior prompt engineers who’ve built and shipped production LLM applications for Fortune 500 enterprises and unicorn startups — engineers who understand not just prompting techniques but the full LLM stack: retrieval-augmented generation, fine-tuning, evaluation frameworks, and cost optimization.

Start in days, not months. Pay 50% less than equivalent US-based AI talent.

What Our Prompt Engineers & LLM Specialists Build

Production RAG Systems

Retrieval-augmented generation pipelines on LangChain, LlamaIndex, and custom retrieval stacks — with chunking strategies, embedding model selection, hybrid search, and re-ranking that actually work at production query volumes.

LLM Application Architectures

Multi-turn conversational systems, agent orchestration frameworks, tool-calling pipelines, and structured output extraction — built to be reliable, auditable, and debuggable at scale.

Prompt Engineering & Optimization

Systematic prompt development using chain-of-thought, few-shot, and reasoning chain techniques. A/B testing frameworks for prompt variants. Guardrail systems that reduce hallucination rates and enforce output formats.

Fine-Tuning & Model Adaptation

LoRA, QLoRA, and full fine-tuning pipelines on domain-specific datasets. RLHF and DPO alignment workflows. Evaluation suites using Evals, RAGAS, and custom benchmark frameworks.

LLM Evaluation & Observability

Production monitoring stacks with LangSmith, Helicone, and custom evaluation loops. Automated regression testing for prompt changes. Cost-per-query optimization and model selection at scale.

LLM Technology Stack

Models: GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3, Mistral, Command R+

Frameworks: LangChain, LlamaIndex, Haystack, Semantic Kernel, CrewAI, AutoGen

Vector Databases: Pinecone, Weaviate, Qdrant, pgvector, Chroma

Fine-tuning: Hugging Face Transformers, Axolotl, Unsloth, OpenAI fine-tuning API

Evaluation: RAGAS, LangSmith, Weights & Biases, DeepEval, Braintrust

Infrastructure: AWS Bedrock, Azure OpenAI, GCP Vertex AI, vLLM, Ollama

Client Success Story: Legal AI Platform — 94% Reduction in Hallucination Rate

A Series B legal-tech startup had built an AI contract analysis tool where hallucinated clauses were appearing in summarized outputs — a critical failure in a regulated industry. Our prompt engineering team redesigned the RAG pipeline with a hierarchical chunking strategy, cross-encoder re-ranking, and a multi-step chain-of-thought reasoning prompt that cited source passages before drawing conclusions. Hallucination rate measured by a custom eval suite dropped from 11% to under 0.7%. The product launched to 40 enterprise law firm customers within 90 days of the engagement starting.

Client Success Story: E-Commerce AI Assistant — $4M ARR in Conversational Commerce

A mid-market e-commerce operator wanted an AI assistant that could guide shoppers through product selection, handle returns, and upsell complementary items — all via natural conversation. Our LLM specialists built a multi-agent system using function calling and a custom intent classification prompt layer that routed queries to specialized sub-agents. The assistant handled 68% of support interactions without human escalation and increased average order value by 23% through contextual product recommendations. The feature drove $4M in attributable ARR within its first year.

Why Companies Choose Our Prompt Engineers

• Production-grade experience: Every specialist has shipped LLM applications to real users — not just built demos
• Full-stack AI fluency: They understand embeddings, vector search, fine-tuning, and inference optimization — not just prompt templates
• Evaluation-first mindset: They build eval suites before shipping, so you know when prompts degrade
• 50% cost savings: Senior LLM expertise at a fraction of US market rates
• Fast start: Most engagements begin within 1–2 weeks of your first call

Engagement Models

• Individual LLM Specialist — One senior prompt engineer embedded in your AI team. Ideal for adding RAG expertise, evaluation rigor, or fine-tuning capability to a team that already has ML depth.
• AI Application Pods (2–4 engineers) — LLM specialist paired with a backend engineer and ML ops in a coordinated squad. Common for teams building new AI products or scaling existing LLM pipelines.
• Full LLM Teams (5–15+ engineers) — Complete squads for large-scale AI platform builds including prompt engineers, ML engineers, and AI infrastructure specialists.
• Contract-to-Hire — Evaluate a specialist’s real output before committing long-term.

How To Vet Prompt Engineers & LLM Specialists

Our vetting identifies engineers who understand LLM behavior deeply — not just copy-paste prompt templates.

1. Technical screening — LLM internals (attention mechanisms, tokenization, context windows, temperature/sampling), RAG architecture trade-offs, chunking strategies, embedding model selection, and fine-tuning approaches. Over 90% of applicants do not pass this stage.
2. System design challenge — Design a production RAG system for a specific domain: legal, medical, financial. Evaluated on retrieval quality, hallucination mitigation, latency, and cost optimization.
3. Live prompting session — Given a failing prompt and eval results, diagnose the failure mode and iterate to a working solution. Assessed on systematic debugging, not intuition.
4. Communication screening — LLM specialists must explain model behavior and limitations to non-technical product teams. We assess this explicitly.

What to Look for When Hiring Prompt Engineers & LLM Specialists

Strong candidates understand why LLMs fail — not just how to make them work in demos.

What strong candidates demonstrate:

• They discuss context window management, token budgeting, and chunking strategy trade-offs with specifics — not just “we used LangChain”
• They’ve built and run evaluation suites using RAGAS, DeepEval, or custom frameworks — they know their hallucination and faithfulness numbers
• They understand the difference between prompt engineering, fine-tuning, and RAG — and when each is the right solution
• They’ve optimized for cost and latency at production scale — they know what a 10,000 query/day system actually costs to run

Red flags to watch for:

• Equates “prompt engineering” with writing good instructions in plain English — has no systematic evaluation approach
• Can’t explain why a RAG pipeline produces hallucinations or how to measure retrieval quality
• Has only used LLMs via chat interfaces, not through APIs or production application code
• No experience with production monitoring or observability for LLM applications

Interview questions that reveal real depth:

• “Walk me through how you’d diagnose and fix a RAG system where 15% of answers contradict the retrieved context.”
• “When would you choose fine-tuning over RAG for a domain-specific application? What data and infrastructure requirements change your decision?”
• “How do you test that a prompt change hasn’t degraded performance on edge cases? Walk me through your evaluation workflow.”

Related Services

• AI Engineers & ML Engineers — Broader AI/ML engineering including model training, infrastructure, and deployment.
• ML Engineers — Machine learning engineers who build and train the models your LLM applications build on.
• MLOps Engineers — Infrastructure and deployment specialists who keep your LLM systems running reliably in production.
• Data Engineers — Build the data pipelines and vector stores that power your RAG systems.

Want to Hire Remote Prompt Engineers & LLM Specialists?

We source, vet, and place senior prompt engineers and LLM specialists who’ve built and shipped production AI applications — engineers who understand evaluation, RAG architecture, and fine-tuning, not just prompting syntax. Whether you need one LLM specialist or a complete AI application team, we make it fast, affordable, and low-risk.

Get matched with LLM specialists →

Ready to hire prompt engineers who’ve shipped production AI? Contact us today and we’ll introduce you to senior LLM specialists within 48 hours.