Senior Software Developer - Machine Learning/Python

Senior Software Developer (Python / Machine Learning)

DXC Technology (NYSE: DXC) helps global companies run their mission-critical systems and operations while modernizing IT, optimizing data architectures, and ensuring security and scalability across public, private and hybrid clouds. The world's largest companies and public sector organizations trust DXC to deploy services to drive new levels of performance, competitiveness, and customer experience across their IT estates. Learn more about how we deliver excellence for our customers and colleagues at DXC.com.

DXC Technology is partnering with a large Australian mining company undergoing a significant transformation of its operations through electrification, automation, and advanced digital optimisation. The client is transitioning toward energy‑efficient, highly autonomous mine sites enabled by renewable energy, battery‑electric equipment, and intelligent real‑time decisioning systems.

DXC is seeking a Senior Software Developer with strong Machine Learning expertise to join a consulting team delivering mission‑critical software platforms that directly support live mining operations. These systems are central to enabling battery‑electric haulage in fully electric pits, including charging orchestration, energy optimisation, dispatch logic, simulation, and real‑time operational decisioning. Reliability, performance, and correctness are paramount in this high‑consequence industrial environment.

As a DXC consultant, you will take ownership of the design and delivery of high‑performance, distributed backend systems, with a strong emphasis on the production integration of machine‑learning‑driven components. You will provide technical leadership, influence architectural direction, mentor engineers, and ensure ML‑enabled services are implemented with production‑grade robustness, observability, and scalability.

This role combines deep backend engineering with applied machine learning and requires strong system design skills, disciplined engineering practices, and the ability to operate confidently in real‑time, operationally critical environments.

Desired Experience

• 8–10+ years’ experience in software design and development of production systems
• Proven experience leading or mentoring software engineering teams
• Strong background in distributed systems and backend development
• Demonstrated experience integrating machine learning models into production environments
• Exposure to high‑availability, real‑time, or operational technology environments (mining or heavy industry experience advantageous)
• End‑to‑end ownership of systems from design through deployment and operational support

Responsibilities

• Design and implement high‑performance microservices and distributed backend systems
• Own the implementation of ML‑driven services, including training pipelines, simulation environments, and real‑time inference integration
• Drive performance optimisation, reliability, and scalability of production platforms
• Lead code reviews and enforce engineering standards and best practices
• Collaborate closely with DXC solution architects, data scientists, DevOps engineers, and client operational teams
• Establish and maintain robust CI/CD pipelines, automated testing, and infrastructure‑as‑code practices
• Ensure high levels of system observability, diagnostics, and operational readiness
• Contribute to technical design artefacts, solution documentation, and implementation plans

Technical Requirements

• Strong Linux systems knowledge (e.g. Ubuntu, RedHat)
• Deep experience building distributed backend systems
• Strong programming capability in Python and at least one of: C++, C#, Rust, or similar languages
• Experience designing and implementing microservices using Docker and Kubernetes
• Experience with message‑driven architectures (e.g. Kafka, RabbitMQ, MQTT)
• Solid understanding of algorithmic performance, latency, and computational constraints
• Experience with CI/CD and infrastructure‑as‑code tooling (e.g. GitHub Actions, Terraform, Artifactory)
• Ability to work with computationally intensive ML components and understand their runtime and performance characteristics