Hire Expert AI
Engineers

An AI engineer is a software professional who designs, builds, and deploys artificial intelligence systems. Their job is to take data, algorithms, and infrastructure, and turn them into production-ready solutions that run inside a business.

Unlike data scientists who focus on analysis or researchers who prototype, AI engineers are responsible for making models scalable, secure, and integrated into real workflows. They write code, build pipelines, deploy on cloud platforms, and continuously monitor performance so that the AI delivers measurable value post-deployment.

An AI engineer designs the architecture, writes the code, and connects the models to your business environment. Their day-to-day work involves preparing and cleaning data, training ML and deep learning models, and deploying them on cloud platforms so they can scale.

They also integrate AI into applications, CRMs, or workflows through APIs and microservices. Once live, they monitor performance, fix drift, and retrain models with new data to keep results accurate.

You can usually hire artificial intelligence engineers right away. Once you share your project goals and requirements, we’ll match you with engineers who have the right skills and availability. In most cases, an initial consultation happens within days and onboarding the engineers into your project takes about one to two weeks depending on scope and complexity.

If it’s a smaller proof-of-concept or a short engagement, you can hire remote AI developers even faster. And for larger, enterprise-scale builds, we’ll schedule a dedicated team and ensure everything is aligned before kickoff, but the process is designed to move quickly so you don’t lose momentum.

You decide. If you want a dedicated AI engineer or team fully embedded in your project, we’ll assign them exclusively to you. They’ll like an extension of your in-house staff (e.g., a contractor). This setup is ideal for long-term builds or when you need constant collaboration.

If you prefer a more flexible model, we also offer shared resources managed through our delivery team. In that setup, engineers divide their time across projects, but you still get consistent communication and guaranteed output.

The latter approach works best for shorter projects, experimental proofs-of-concept, or when you only need part-time support. Either way, you get access to proven AI talent, and we adapt the engagement model to match your budget, timelines, and goals.

Absolutely. Our AI engineers are used to working side-by-side with in-house developers, product teams, and IT staff. We adapt to your processes, whether you use Agile sprints, standups, or ticket-based workflows. Engineers can join your Slack, Jira, or GitHub environments so collaboration feels seamless.

Our engineers have delivered AI solutions across a wide range of industries, applying technical expertise to very different use cases and business models. So when you hire an artificial intelligence developer through us, you’re getting domain-specific expertise.

In finance, we’ve built fraud detection engines, predictive credit models, and automated compliance monitoring tools. We’ve also worked extensively in retail and e-commerce, creating recommendation engines, dynamic pricing systems, and computer vision tools for inventory tracking.

In logistics and supply chain, we’ve deployed predictive delivery models, route optimization systems, and real-time anomaly detection. Our work in SaaS and technology spans custom NLP solutions, generative AI integrations, and large-scale machine learning platforms.

Creative industries like marketing, design, and media have tapped our engineers for text-to-image, generative video, and personalized content automation. And in manufacturing, we’ve implemented predictive maintenance, defect detection, and industrial automation powered by computer vision and machine learning.

You can hire ML engineers for short-term projects and long-term ones. You might have a one-off app you’re trying to build for a specific workflow or need an ongoing engagement with a team of devs. Either way, we have the resources and a pricing model specificall for you.

Our engineers are senior-level specialists with years of hands-on experience across machine learning, NLP, computer vision, generative AI, and automation. Every engineer is thoroughly vetted through technical screenings, real-world project assessments, and industry references before joining our network.

In our organization alone, they’ve worked on more than 2,500 AI projects for startups, enterprises, and research institutions. Most were hired on with 5 to 10+ years of experience. So, when you hire through us, you’re not getting junior or untested talent.

Yes. Their role is to translate technical progress into business updates, keep milestones on track, and ensure smooth communication between your internal stakeholders and our engineering team.

Some clients prefer direct access to engineers only, but for intricate, multi-phase, and enterprise-scale builds, we recommend bringing on a project manager to keep delivery efficient and aligned with your overarching strategy.

Costs depend on the engagement model, project scope, and the level of expertise required. Short-term projects and proofs of concept typically start at lower, project-based rates, while long-term dedicated teams are priced monthly or quarterly.

On average, you should expect senior AI engineers to cost more than generic developers because of their specialized skill set, but outsourcing through us is significantly more cost-effective than hiring full-time in-house talent.

During your consultation, we’ll give you a clear estimate based on your goals and budget.

We don’t provide free trial periods, because AI engineering requires planning, testing, and delivery of value from day one, which we can’t refund. However, we do often recommend starting with a smaller, scoped proof-of-concept project.

This way, you (and we) validate the technology, confirm business impact, and build internal confidence before committing to a larger rollout. It’s not a “trial,” but a low-risk way to start smart and scale responsibly.