Hire AI Engineers Who Build Intelligent Systems That Work in Production

An AI engineer builds and ships AI systems into production: machine learning models, RAG and retrieval pipelines, LLM integrations, computer vision, and AI agents, along with the evaluation, guardrails, and monitoring that keep them reliable. A data scientist leans more toward analysis, experimentation, and modeling in notebooks. You often need both, but if the goal is a working AI feature inside your product, an AI engineer is the one who ships it.

It depends on seniority, the engagement model, and scope rather than a fixed rate card. A dedicated engineer is billed at a monthly rate, a managed team is priced by its composition, and a project-based engagement is a fixed quote against a defined scope. The biggest cost driver is the complexity of the AI work: a RAG integration on foundation models costs far less than training and serving custom models with strict latency or compliance needs. We give you a clear quote in a free consultation before any commitment, and you can scale up or down without lock-in.

We can match you with a vetted AI Engineer within a week. Our team includes pre-screened engineers with production experience in AI, so we skip the lengthy recruitment cycle and get straight to onboarding.

Three options: dedicated developers who work exclusively on your project, a managed team where we handle delivery end-to-end, or a project-based engagement with fixed scope and timeline. All models include a technical lead and regular progress updates.

Every AI engineer is tested on production AI work, not notebooks: a RAG or retrieval exercise (chunking, embeddings, reranking), a prompt and eval design task, a system-design round on inference cost, latency, and fallbacks, and a trial project. We also check how they reason about hallucination control, guardrails, and human-in-the-loop checkpoints.

Yes. We share detailed profiles including relevant project experience, then arrange a technical interview so you can assess fit before committing. If the match is not right, we provide alternatives at no cost.

We offer a replacement guarantee. If the developer does not meet expectations within the first two weeks, we reassign and provide a replacement with no additional charges or delays to your project timeline.

Both. For many use cases, fine-tuned foundation models (GPT-4, Claude, open-source LLMs) deliver excellent results at lower cost than training from scratch. When your domain requires specialized capabilities that general models cannot provide, we train custom models using your data. We advise on the right approach during discovery.

We design AI systems with data privacy built in. This includes on-premise or private cloud deployment options, data anonymization pipelines, access controls, and compliance with GDPR, HIPAA, or industry-specific regulations. For LLM integrations, we offer self-hosted model options that keep your data off third-party servers entirely.

Look past framework names to production judgment. Strong AI engineers know Python and the ML stack such as PyTorch, TensorFlow, and Hugging Face, but the real differentiator is how they handle the hard parts: retrieval and embedding design for RAG, prompt and evaluation pipelines, hallucination control and guardrails, inference cost and latency trade-offs, and human-in-the-loop checkpoints for critical decisions. Ask for production examples rather than notebooks, which is exactly how we vet every engineer we place.

Our engineering team is based in India and works with clients across the US, Canada, Europe, Australia, and the Middle East. Engineers align to your working hours for overlap on standups, reviews, and planning, then default to async communication and documented decisions for the rest, so progress stays visible without forcing anyone onto a permanent night shift.