AI Software Engineer

SoftwareOne is a global provider of software and cloud solutions.

With a presence in over 70 countries and more than 12,000 professionals, we help organizations optimize software investments, modernize applications, and unlock the value of cloud, data, and AI.

Our people are at the core of everything we do.

We enable collaboration across borders, continuous learning, and opportunities to grow in a fast-evolving technology landscape.

Whether your focus is on technology, customer success, or business operations, your ideas matter, and your contributions make a difference.

Join a global team where you can build your skills, work with leading technologies, and make a real impact for our customers.

Are you passionate about building AI solutions that solve real customer challenges?

Do you enjoy taking AI products from architecture and development through to deployment, monitoring, and optimization?

Are you excited by the opportunity to work across cloud, MLOps, and modern AI technologies to deliver measurable customer value?

At SoftwareOne, we help organizations unlock the value of cloud, data, and AI through innovative technology solutions.

We are looking for an AI Software Engineer to design, build, deploy, and optimize production-ready AI solutions that help customers achieve their business goals and accelerate AI adoption.

As an AI Software Engineer, you will work across the full AI product lifecycle, from solution architecture and model deployment to performance optimization and ongoing reliability.

Collaborating closely with data scientists, engineers, and customer-facing teams, you will help transform AI concepts into scalable, secure, and high-performing production solutions.

Your key responsibilities:

• Designing, deploying, and optimizing AI and machine learning solutions in production environments.
• Building scalable pipelines for real-time inference, batch processing, and model-serving use cases.
• Developing secure and reliable APIs and services that expose AI capabilities to customers and business applications.
• Implementing MLOps practices, including CI/CD pipelines, monitoring, logging, alerting, and performance tracking.
• Collaborating with data scientists and technical teams to convert research models into production-ready solutions.