Hire Generative AI Engineers

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.

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.

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.

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.

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.

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.

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.

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.