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.

• 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).
• Partner with MLOps and DevOps teams to implement CI/CD pipelines, model versioning, monitoring, retraining, and production support best practices.
• Collaborate cross‑functionally with Data Engineering, Software Development, Sales & Pre‑Sales, and leadership stakeholders, while staying current with emerging ML technologies and driving adoption where valuable.

• Proven experience delivering machine learning solutions into production environments.
• Strong technical expertise in Python and ML frameworks such as TensorFlow, PyTorch, and Scikit‑learn.
• Solid understanding of end‑to‑end ML pipelines, data preprocessing, feature engineering, model evaluation, and monitoring.
• Experience working with cloud platforms (Azure preferred), Docker, Kubernetes, and MLOps tooling (e.g., MLflow, Airflow).
• Ability to balance technical depth with leadership responsibilities and commercial awareness and having experience working within agile delivery environments and across multiple stakeholder groups