Hire AI Engineers

RaftLabs engineers cover the full AI engineering stack: RAG pipeline engineers who design retrieval systems and evaluation frameworks; LLM fine-tuning engineers who handle dataset curation, training runs, and model evaluation; AI agent architects who build multi-step agent systems with tool use and failure handling; voice AI engineers with STT and TTS pipeline experience; MLOps engineers who build serving infrastructure and retraining pipelines; and AI product engineers who build the user-facing product layer on top of AI models.

A machine learning engineer focuses on building and training models: data pipelines, feature engineering, model selection, and evaluation. An AI engineer works at the application layer: integrating LLMs into products, building RAG systems, designing agent workflows, handling prompt engineering and output validation, and deploying systems that use pre-trained models rather than training from scratch. The distinction matters for scoping: if you are deploying and integrating AI, you need an AI engineer. If you are training custom models on proprietary data, you need an ML engineer. Most production systems need both.

Both. Fixed-cost project engagements work well when you have a defined AI use case with clear scope - a RAG system, an agent workflow, a voice AI interface. Dedicated team embedding works when you have ongoing AI development needs across multiple features or product lines and want engineers who build context about your system over time. We recommend starting with a scoped first project in either case; it proves the fit before you commit to a longer arrangement.

Production stack includes LangChain and LangGraph for agent orchestration; Pinecone, Weaviate, Qdrant, and pgvector for vector storage; OpenAI, Anthropic, and Google Gemini APIs; Llama for open-source deployments; Whisper and Deepgram for speech-to-text; ElevenLabs and Azure Cognitive Services for text-to-speech; FastAPI and BentoML for model serving; MLflow for experiment tracking; Evidently AI for model monitoring; and Airflow and Prefect for pipeline orchestration.

For a scoped first project, we can typically start within two weeks of a signed agreement. We use the first conversation to understand the use case, identify which engineering disciplines are involved, and define a clear first-project scope. If your use case involves a technology area where all engineers are currently engaged, we are transparent about that rather than overpromising availability.

A scoped AI project - RAG pipeline, agent system, voice AI interface - typically runs $25,000 to $100,000 depending on scope and complexity. Dedicated AI engineering team embedding starts at $12,000 to $18,000 per month for a senior AI engineer with part-time PM. A team of two engineers plus PM runs $24,000 to $36,000 per month. We provide fixed-cost proposals after a scoping session, not hourly estimates.

Your infrastructure. Engineers work in your cloud accounts, your repository, and your deployment pipelines. All code and configuration is owned by you from day one. We do not maintain a proprietary platform that creates lock-in. At the end of an engagement, a competent engineer on your team can pick up and continue without any extraction process.