Hire AI Engineers: Scale Your Team with Top Artificial Intelligence Experts

Rates vary by seniority, AI specialization, and scope. In the US, an in-house AI engineer costs $150K–$312K+/year in salary alone, before benefits, equipment, and overhead. AI talent commands a 15–30% premium over general software engineers due to the specialized math, data, and model-building skills required. Outstaffing is significantly lower; for a detailed estimate, share your requirements, and we'll provide transparent pricing.

With DBB Software, you'll receive matched CVs within 48 hours of sharing your requirements. Full onboarding typically takes 1–2 weeks. Compare that to the industry average of 41–66 days for traditional hiring, and that's before the 3–6-month ramp-up period where new hires operate at reduced productivity. You can have an experienced AI professional working on your models within days.

A strong AI developer combines programming proficiency (Python is the standard, plus SQL and cloud platforms), deep understanding of mathematics (linear algebra, calculus, probability, statistics), and hands-on experience with ML frameworks like PyTorch and TensorFlow. Beyond tools, look for professionals who can articulate trade-offs between model architectures, understand when a simpler approach outperforms a complex one, and have deployed AI models to production, not just trained them in Jupyter notebooks.

Yes. Our project-based model handles defined-scope AI work: building a recommendation engine, setting up an NLP pipeline, running a computer vision proof of concept, or fine-tuning a large language model. For ongoing work, staff augmentation lets you hire remote AI engineers as long as you need them, with flexible scaling. Whether you need an AI developer for 3 months or a full team for a year, we match the engagement to your timeline.

Yes. Our team is based in Europe, offering a strong overlap with US and EU business hours. You'll have daily standups, shared Slack channels, and real-time collaboration. For AI projects specifically, this means shared access to experiment tracking dashboards, model registries, and data pipelines during your working day.

Software engineers build applications. AI professionals build the intelligent systems inside those applications: the models that classify, predict, generate, or recognize. This requires a distinct skill set: statistics, linear algebra, model architecture design, data preprocessing, and familiarity with specialized frameworks like PyTorch, TensorFlow, and LangChain. An AI professional typically costs 15–30% more than a general software engineer because of this specialization.

The best AI professionals are also strong software developers. They write clean, testable code, work with CI/CD pipelines, and deploy to production. Our AI professionals bridge both worlds.

Every candidate goes through a multi-stage screening process: technical assessments covering machine learning theory and applied problem-solving, framework-specific coding challenges (PyTorch, TensorFlow, or LangChain depending on specialization), portfolio review of deployed AI projects, and soft skills evaluation. We look for professionals who have shipped AI to production, not just completed online courses.

Our teams have production experience across the full AI stack: deep learning frameworks (TensorFlow, PyTorch), generative AI models (GPT-4/5, Claude, Llama, Gemini, Grok), GenAI tooling (LangChain), MLOps (Kubeflow), vector databases (pgvector), and cloud AI services (AWS Bedrock, AWS Rekognition, GCP AutoML, GCP Document AI, Azure OpenAI Service, Azure Bot Framework).

Core techniques include RAG, prompt engineering, fine-tuning, embeddings, and vector search. Whether you need a custom model or a generative AI application, we match specialists to your specific AI stack.

Yes. One of the main advantages of outstaffing is flexibility. You can hire one AI specialist to start and scale up as your project grows, or scale down after a model reaches production. We handle recruitment and onboarding on our side, so adding new team members is fast and doesn't require you to run a separate hiring process.

It depends on the scope. A rough sizing guide:

• 1 AI engineer: Single-model projects, proof of concept, fine-tuning an existing LLM, adding AI features to an existing product

• 2–3 engineers: Multi-model systems, end-to-end ML pipelines, NLP + recommendation engine combinations, production MLOps setup

• 4+ engineers: Enterprise AI platforms, multiple AI workstreams, custom model training at scale, 24/7 monitoring and retraining

A single senior ML engineer can handle model development, training, and deployment for a focused use case. Larger AI initiatives require a team with complementary specializations across ML engineering, data engineering, and MLOps.

We take performance seriously. If someone isn't meeting expectations, we work with you to address the issue quickly through additional mentoring, reassigning responsibilities, or replacing them with a better-suited professional. Swaps typically happen within 1–2 weeks, not months.

Not necessarily. If you have a CTO or tech lead on your side, our specialists report to your leadership directly. If you don't have in-house technical leadership, we can provide a team lead or project manager who handles day-to-day coordination, architecture decisions, and delivery oversight. Either way, you stay in control of priorities and product direction. Many of our clients hire AI talent through us specifically because we handle the technical screening.

You can hire pre-vetted AI engineers through DBB Software. We maintain a team of skilled AI professionals with production experience across machine learning, generative AI, NLP, and computer vision. Unlike freelance marketplaces where you screen candidates yourself, we handle vetting, technical assessments, and matching, and deliver shortlisted profiles within 48 hours of your request.