Hire AI Engineers — LLM, ML & Deep Learning Experts

An AI engineer bridges machine learning research and production software engineering. They take AI capabilities — large language models, computer vision models, recommendation systems — and build reliable, observable, cost-efficient production systems around them. Unlike a data scientist focused on model development, an AI engineer focuses on deployment, integration, and the software infrastructure that makes AI work reliably for users.

Standard start time is 48 hours from contract signing. For AI engineers with specific specialisations — LLM fine-tuning, computer vision, reinforcement learning — a one-week lead time is typical. AI engineering is a fast-moving field and we keep our bench current with practitioners building in production today.

API integration is the right starting point for most products — it is fast, cheap to iterate on, and benefits from continuous model improvements by the provider. Fine-tuning a custom model makes sense when you have domain-specific terminology that general models handle poorly, when output format consistency is critical, when token costs at scale are prohibitive, or when data privacy prevents sending content to a third-party API. We help you make this call based on your specific requirements and constraints.

Retrieval-Augmented Generation (RAG) grounds LLM responses in documents retrieved from your knowledge base — reducing hallucination and enabling responses about private data the base model was not trained on. You need RAG if your AI feature needs to answer questions about your company documentation, customer data, product catalogue, or any information that was not in the model's training data. Kultrix AI engineers design RAG pipelines with chunk strategy, embedding model selection, and retrieval quality evaluation.

Hallucination is managed through a combination of architecture and evaluation. RAG architectures constrain the model to retrieved context rather than parametric knowledge. System prompt engineering sets explicit instructions to cite sources and acknowledge uncertainty. Output validation checks structured outputs against schemas. Evals measure hallucination rate systematically across a test set so regressions are caught before deployment. No approach eliminates hallucination entirely — we are transparent about the residual risk.

Yes. We build AI agents that can execute multi-step tasks autonomously — using tools like web search, code execution, database queries, and API calls. We implement agents with LangGraph or the OpenAI Assistants API, design robust error handling and human-in-the-loop checkpoints for high-stakes actions, and instrument agent traces with LangSmith or Weights & Biases for observability.

LLM costs can scale unexpectedly in production. We implement semantic caching with Redis to avoid re-running identical prompts, select the smallest model sufficient for each task (not every call needs GPT-4 level capability), compress context with summarisation for long conversation histories, batch non-urgent calls, and set token budget guardrails. We configure cost dashboards from day one so surprises do not appear on the invoice.

Yes. Adding AI capabilities to an existing product — a search box, a document summariser, a copilot sidebar — is one of the most common AI engineering engagements. We design the integration architecture, handle streaming responses for perceived performance, manage session context, and add AI features behind feature flags so rollout is controlled.

LLM evaluation requires a purpose-built test suite because traditional software assertions do not apply to natural language outputs. We build golden dataset evals — curated input-output pairs for the most important user interactions — and measure response quality with LLM-as-judge scoring, exact match for structured outputs, and human annotation for nuanced cases. Evals run in CI before every prompt or model change.

Yes. For data-privacy-sensitive applications, we deploy open-source models (Llama 3, Mistral, Phi) on private cloud infrastructure using vLLM or Ollama. This keeps data within your security perimeter while still providing competitive LLM capability. We benchmark private deployment performance against API options and provide an honest cost-performance comparison before recommending either path.

Yes. We implement data handling practices that comply with GDPR — minimising what is sent to third-party APIs, obtaining appropriate consent for AI processing, providing data deletion mechanisms, and documenting AI processing activities in your Record of Processing Activities. For healthcare and financial services, we design AI architectures that keep sensitive data entirely within your infrastructure.

We offer project-based engagements (build a defined AI feature with fixed scope and timeline) and monthly retainer engagements (ongoing AI engineering capacity for continuous development). Both include weekly reporting, access to your account manager, and transparent billing. Minimum engagement is one month. Most production AI integrations take four to twelve weeks from architecture to launch.