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Hire Generative AI Engineers

Hire generative AI engineers from Neural AI. LLM specialists, prompt engineers, and RAG developers for ChatGPT, Claude, Gemini.

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/ the solution /

Hire Generative AI Engineers built around your business.

Every solution we deliver is built on three pillars: your data, your context, and continuous improvement. Each capability is traceable and measurable.

  • LLM & Foundation Model Expertise

    Engineers experienced with GPT-4, Claude, Gemini, Llama, Mistral, and other foundation mod…

  • RAG System Development

    Specialists in Retrieval-Augmented Generation architectures including vector database sele…

  • Prompt Engineering & Optimisation

    Systematic prompt engineering that goes beyond trial and error to deliver consistent, high…

  • GenAI Application Architecture

    End-to-end architecture for generative AI applications including token management, streami…

/ how it works /

Live in weeks, not months.

We assess your generative AI use case to determine technical feasibility, appropriate model selection, RAG requirements, and architectural approach. This assessment prevents wasted effort on approaches unlikely to succeed.

We match engineers with the specific GenAI expertise your project requires, whether RAG development, fine-tuning, multi-agent systems, or application integration. Skills are matched to use case requirements and your existing technology stack.

Engineers build functional prototypes quickly to validate the approach, demonstrate capability, and gather feedback before committing to full production development. Prototypes include evaluation benchmarks that measure output quality.

Engineers design production architecture with proper error handling, content safety, cost management, monitoring, and scalability. Architecture decisions are documented for your team with rationale and trade-off analysis.

Engineers build the production system with comprehensive evaluation pipelines that measure response quality, relevance, safety, and consistency. Automated evaluation ensures quality standards are maintained as models and prompts evolve.

Engineers deploy the system with monitoring, alerting, and operational runbooks. Knowledge transfer ensures your team can maintain, update prompts, and extend the system independently.

/ what you get /

Everything you need. Nothing you don't.

LLM & Foundation
Model Expertise
RAG System
Development
Prompt Engineering
& Optimisation
GenAI Application
Architecture
/ built for your situation /

Sounds familiar?

CTO, scale-up company
"We need a senior AI engineer to build our RAG-based knowledge assistant but we can't justify a full-time hire yet — we're looking for someone 3 days a week for 3 months"

We place a senior AI engineer with RAG and LLM integration experience directly into your team on a flexible retainer — typically 8–12 days/month with same-week availability.

How Neural AI helps

We place a senior AI engineer with RAG and LLM integration experience directly into your team on a flexible retainer — typically 8–12 days/month with same-week availability.

/ proof /

Real deployments. Real results.

Generative AI & RAG

Ligi.ai Legal Sector

Neural AI built Ligi.ai, a custom AI legal assistant for Maltese law firms that combines retrieval-augmented generation with deep knowledge of Maltese legislation. The system assists lawyers with document drafting, legal research across case law, and document review, reducing research time by over 70%.

GenAI engineers building production legal RAG platform

Read case study →
Document AI

ARB Document Processing

OCR and Document AI solution converting digital documents into structured database information for Power BI processing, handling Maltese and English text with high accuracy.

LLM-powered document analysis and extraction system

Read case study → Data Engineering & AI

GPT Cloud Migration

Complete migration of Malta Tourism Authority legacy licensing data to cloud using GPT-powered NLP for error detection, achieving over 90% reduction in migration errors.

GenAI platform architecture and cloud deployment

Read case study →
/ questions /

Hire Generative AI Engineers FAQ

What generative AI models do your engineers work with?
Our engineers work with all major foundation models including OpenAI GPT-4 and GPT-4o, Anthropic Claude, Google Gemini, Meta Llama, Mistral, and other open-source models. They understand the strengths, limitations, and optimal use cases for each model and can architect multi-model solutions that use the best model for each task.
What is RAG and why is it important?
Retrieval-Augmented Generation grounds LLM responses in your specific data by retrieving relevant documents before generating answers. Without RAG, models hallucinate or provide generic responses. Our RAG engineers build systems that retrieve accurately from your knowledge base and generate responses grounded in your actual business information.
How do you handle LLM hallucination?
We implement multiple layers of hallucination mitigation: RAG grounding against source documents, output validation checks, citation requirements, confidence scoring, and human-in-the-loop review for high-stakes applications. Our evaluation frameworks measure hallucination rates and track improvement over time.
Can your engineers fine-tune models for our use case?
Yes, our engineers handle supervised fine-tuning, RLHF, DPO, and LoRA approaches for both commercial and open-source models. Fine-tuning is recommended when prompting alone cannot achieve required output quality, when you need to optimise for cost by using a smaller fine-tuned model, or when you need specific output formats consistently.
How do you manage generative AI costs?
We implement token optimisation, response caching, model routing that uses cheaper models for simpler queries, prompt compression, and batch processing for non-real-time workloads. Our cost management typically reduces GenAI API costs by 40-60% compared to naive implementations.
What about content safety and guardrails?
We implement input filtering, output validation, content classification, and safety guardrails that prevent inappropriate, harmful, or off-topic responses. For regulated industries, additional compliance checks ensure outputs meet industry-specific requirements.
Can engineers integrate GenAI into our existing applications?
Yes, our engineers handle the full integration lifecycle from API design through frontend development to deployment. They build GenAI features into web applications, mobile apps, internal tools, and customer-facing platforms using streaming responses, progressive loading, and responsive UI patterns.
What is the difference between hiring a GenAI engineer and an AI engineer?
GenAI engineers specialise in large language models, RAG systems, prompt engineering, and the unique challenges of generative AI production systems. General AI engineers cover broader ML including traditional classification, regression, and computer vision. If your project specifically involves LLMs and text generation, GenAI specialists deliver faster results.
/ get started /

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