The problem is not a shortage of AI engineers. It is a shortage of AI engineers who have shipped production AI systems. Most candidates have done research or played with APIs. Very few have debugged latency at production volume, built evaluation frameworks, or handed over systems that other teams can actually operate.
RaftLabs is a team of AI engineers who have shipped 100+ production systems across RAG pipelines, AI agents, voice AI, and custom ML. When you hire from or with us, you are accessing engineers who have crossed the demo-to-production gap many times -- and know exactly where that gap opens up.
100+ production AI systems shipped -- not demos, not pilots
Engineers with hands-on experience in RAG, agents, fine-tuning, and voice AI
Fixed-cost project engagements or dedicated team embedding
Clients include Vodafone, Cisco, T-Mobile, and Nike
Recent outcomes
Voice AI · Research
Text-based interviews converted to automated phone calls
6× deeper insightsAI Automation · Ops
Manual invoice OCR across 40+ gas stations
20k+ txns day oneLoyalty · Retail
SuperValu & Centra loyalty platform with receipt validation
1,062 users in 4 weeksSaaS · Logistics
Multi-carrier shipping hub for Indonesian eCommerce
2,000+ shipments yr 1Recognition
Six months into your AI project and still waiting for a senior AI engineer who 'understands production deployment'?
Tried freelancers who build impressive demos that can't handle your data or your scale?
In short
Hiring AI engineers through RaftLabs means accessing engineers who have shipped over 100 production AI systems -- RAG pipelines, AI agents, LLM fine-tuning, voice AI, and MLOps infrastructure. Engagements run as fixed-cost projects or dedicated team embedding. RaftLabs engineers have worked with clients including Vodafone, Cisco, and T-Mobile. Start with a scoped first engagement to prove the fit before committing to longer-term work.
Trusted by
Most AI projects fail between the demo and the first production deployment. The demo works because the inputs are controlled, the data is clean, and the evaluation is informal. Production fails because query distribution is different, latency requirements are strict, data quality varies, and nobody built the monitoring to know when the system stops working.
LinkedIn's 2024 Jobs on the Rise report found AI specialist roles grew 74% annually since 2015, making experienced AI engineers among the hardest technical roles to source. The number of people who can build a compelling AI demo has grown rapidly. The number who have debugged a RAG pipeline degrading silently in production, or rebuilt an agent system after a tool-use failure cascade, is still small.
The specific skills that separate a production AI engineer from a capable researcher are learnable only through shipping. Chunk size tuning in a retrieval pipeline is a judgment call made easier by having seen five retrieval quality failures. Agent failure handling is designed better by someone who has watched an agent loop infinitely on an ambiguous tool response. This is not book knowledge.
| Dimension | Freelance AI developer | Staffing agency placement | RaftLabs dedicated AI team |
|---|---|---|---|
| Production AI experience | Variable, often demo-level | Often unclear until work starts | 100+ production systems shipped |
| RAG, agents, fine-tuning depth | Typically one specialty | Matched by keywords, not outcomes | Multi-discipline engineers per engagement |
| Evaluation and monitoring | Rarely included | Not typically in scope | Standard part of every build |
| Fixed-cost delivery | Rarely | Almost never | Yes, for scoped projects |
| Clients include enterprises | Uncommon | Sometimes | Vodafone, Cisco, T-Mobile, Nike |
| Onboarding time | 2--4 weeks | 4--8 weeks | 1--2 weeks for scoped projects |
Capabilities
Engineers who design full retrieval pipelines for production: document ingestion, chunking strategy, embedding model selection, vector database setup, hybrid search combining dense retrieval with BM25 keyword scoring, and re-ranking. They build evaluation frameworks using RAGAS -- context precision, context recall, answer faithfulness -- and run regression tests when prompts or embedding models change. Production RAG is not plug-and-play; these engineers have tuned pipelines on domain-specific corpora and know where retrieval quality breaks down.
Engineers who handle the full fine-tuning pipeline: dataset curation and labelling, base model selection (Llama, Mistral, Falcon), supervised fine-tuning and instruction tuning, RLHF implementation where needed, model evaluation against domain-specific benchmarks, and deployment of fine-tuned models via vLLM or TGI. Fine-tuning makes sense when a general model's accuracy on your specific task is insufficient and you have sufficient labelled data. These engineers have run production fine-tuning jobs and know when fine-tuning is the wrong approach.
Engineers who design multi-step agent systems: tool definition, LangGraph orchestration for stateful workflows, parallel tool execution, failure handling for tool errors and ambiguous LLM outputs, human-in-the-loop checkpoints for high-stakes decisions, and production monitoring for agent runs. They have shipped agents that operate in real enterprise environments -- querying databases, calling APIs, processing documents -- not just demos. The architecture decisions that determine agent reliability are invisible in a demo and obvious in production.
Engineers with speech-to-text (Whisper, Deepgram), text-to-speech (ElevenLabs, Azure Cognitive Services), and real-time audio pipeline experience. They optimise for conversational latency -- the gap between end of speech and start of response -- and handle interruption, silence detection, and turn-taking in live audio streams. Voice AI is a demanding real-time system where latency requirements are unforgiving. These engineers have shipped voice interfaces for customer support and phone automation at production call volumes.
Engineers who build the infrastructure that makes AI systems operable: model serving via FastAPI or BentoML, CI/CD pipelines for model deployment, feature stores to eliminate training-serving skew, experiment tracking via MLflow, data drift monitoring via Evidently AI, and automated retraining pipelines triggered by drift signals. A model without monitoring is not a production model. MLOps engineers are the difference between an AI system you can run safely and one you hope is still working.
Full-stack engineers who build the user-facing product layer on top of AI models -- not just the model integration, but the interface, the streaming output rendering, the citation display, the error states, the feedback collection, and the session management. Most AI teams have the model layer covered; the product layer is often an afterthought. These engineers have shipped AI products where the engineering of the experience is as important as the quality of the underlying model.
Tell us what you are building, which AI capabilities are involved, and what production looks like for your use case. We will identify the right engineers and scope a first project.
Process
We start with the AI use case, not a job description. We need to understand what you are building, which AI capabilities are involved (RAG, agents, fine-tuning, voice, ML), what your data looks like, and what production means for your use case -- latency requirements, volume, monitoring obligations. This takes one conversation, typically 45 to 60 minutes. It is more useful than a CV screen.
Based on the use case and stack, we identify which engineers on our team fit the specific technical requirements and domain. We are transparent about depth: if your use case requires RLHF fine-tuning and we have stronger coverage in RAG and agents, we say so. You see profiles and backgrounds before committing to an engagement.
We recommend starting with a fixed-cost, time-boxed first project -- typically four to eight weeks -- that proves the fit before longer-term embedding. The first project has a defined scope, a clear success criterion, and a handover at the end. If it works well, we discuss what a continued engagement looks like. If it does not, you have spent a fraction of a long-term contract finding that out.
What clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
CanadaI found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support.
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AI Development
End-to-end AI systems from model selection to production deployment.
Generative AI Development
LLM-powered products, RAG pipelines, and content automation systems.
AI Agent Development
Autonomous agents that plan and execute multi-step tasks inside your workflows.
Machine Learning Development
Custom ML models for prediction, classification, and anomaly detection.
Hire ML Developers
ML developers with production model training, serving, and monitoring experience.
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.
Work with us
We scope Hire AI Engineers in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
