Machine Learning Engineer Team Lead

SoftwareOne and Crayon have come together to form a global, AI-powered software and cloud solutions provider with a bold vision for the future.

With a footprint in over 70 countries and a diverse team of 13,000+ professionals, we offer unparalleled opportunities for talent to grow, make an impact, and shape the future of technology.

At the heart of our business is our people.

We empower our teams to work across borders, innovate fearlessly, and continuously develop their skills through world-class learning and development programs.

Whether you're passionate about cloud, software, data, AI, or building meaningful client relationships, you’ll find a place to thrive here.

Join us and be part of a purpose-driven culture where your ideas matter, your growth is supported, and your career can go global.

Passionate about leading high‑impact machine learning initiatives and delivering scalable, production‑ready AI solutions?

Motivated to mentor and grow a team of ML and data engineers while remaining hands‑on with advanced model development and deployment?

Inspired to join a collaborative Data & AI organisation that values innovation, technical excellence, and continuous learning?

As our new Machine Learning Engineer – Team Lead, you will play a pivotal role in leading the design and delivery of machine learning solutions end‑to‑end, while providing technical leadership and mentorship to a small, high‑performing engineering team.

You will bridge business objectives with advanced ML techniques, guide architectural decisions, and ensure models are production‑ready, scalable, and continuously improved.

will include:

• Lead and mentor a team of ML and Data Engineers, providing technical guidance, regular feedback, and supporting professional development.
• Own the end‑to‑end lifecycle of machine learning initiatives, translating business requirements into well‑defined ML problem statements, technical solutions, and delivery plans.
• Design, build, train, and evaluate advanced machine learning models, and contribute to architectural decisions for scalable ML pipelines and cloud‑based deployments (Azure preferred).