Machine Learning Engineer

As a Machine Learning Engineer, you will drive high-impact initiatives that turn global operational pain points into production-ready ML products. Leveraging our growing global footprint of real-time digital data—from IoT-tagged ULD movements to our global MRO network—you will help build the predictive engine of our business.

Acting as the bridge between complex data streams and real-world aviation impact, you will collaborate with technical and operational teams to scale our forecasting, predictive analytics, and computer vision capabilities. We aren’t just looking for someone to deploy models; we need a passionate specialist with a deep ML toolkit who thrives in ambiguity and is energized by transforming business operations through automation and artificial intelligence.

Key Responsibilities

End-to-End Analytical Initiative Ownership

Drives results, Action Oriented, Ensures Accountability

• Take ownership of ML initiatives from requirements gathering to design, implementation and delivery.
• Structure ambiguous business problems into defined projects and initiatives.
• Define the scope, milestones, and deliverables for assigned initiatives.
• Manage timelines and proactively communicate progress, risks, and dependencies.
• Ensure solutions are delivered to agreed quality and performance standards.

Stakeholder Engagement & Requirement Translation

Customer Focus, Collaborate, Courage

• Engage directly with non-technical stakeholders to clarify operational and business requirements.
• Translate business needs into structured technical specifications.
• Manage expectations by clearly communicating trade-offs, constraints, and solution limitations.
• Facilitate alignment between business users and technical stakeholders.
• Challenge unclear or inconsistent requirements constructively when necessary.

Model Development & Technical Implementation

Drives results, Action Oriented

• Design and implement business logic, gather, refine, and transform structured and non-structured data using Python, SQL, and other ML frameworks.
• Develop and maintain data pipelines to support model training, evaluation, and deployment.
• Apply feature engineering, model selection, training, and evaluation techniques appropriately.
• Ensure code is readable, maintainable, and aligned to agreed engineering standards.
• Own deployment and management of solutions into production or operational environments.

Data Exploration, Validation & Quality Assurance

Ensures Accountability, Drives results

• Perform exploratory data analysis to understand data patterns, limitations, and quality issues.
• Validate data inputs, transformations, and outputs before deployment.
• Conduct testing to ensure robustness and reliability of models and calculations.
• Identify and escalate data inconsistencies or structural issues.
• Maintain documentation of assumptions, validation steps, and methodologies used.

Scalability, Performance & Continuous Improvement

Business Insights, Action-Oriented

• Ensure implemented solutions are scalable and maintainable within the existing data ecosystem.
• Identify performance bottlenecks and implement optimisations where required.
• Contribute to improving analytical techniques, coding standards, and ML best practices.
• Support refinement of documentation, reusable components, and process templates.
• Contribute to strengthening analytical capability within the ML function.

Cross-Functional Collaboration & Knowledge Sharing

Collaborate, Courage

• Work closely with Data Science Specialists and analytics colleagues to align with broader ML strategy.
• Support peer reviews of models and code.
• Share insights and lessons learned from completed initiatives.
• Contribute to a collaborative and solution-focused team culture.
• Contribute to the adoption of AI within the analytics team.

Our Values in Action:

• Be humble and curious Continuously seeks to understand business context, data behaviour, and technical improvements.
• Inspire, empower, and prosper Builds confidence in analytical outputs through reliability, clarity, and professional conduct.
• Team up to be better Works collaboratively across technical and business teams to deliver aligned outcomes.
• Be passionate about our customers Focuses on delivering analytical solutions that create measurable operational and business value.
• Take ownership and get things done Takes responsibility for delivering complete, high-quality solutions from start to finish.
• Be eager to win Strives for impactful results through accuracy, optimisation, and performance improvements.
• Build a better future Contributes to stronger data practices, scalable systems, and improved analytical capability over time.

Small Print

The Machine Learning Engineer role focuses on delivering structured, scalable, and production-ready analytical solutions to support business decision-making and operational improvement. The role requires independent ownership of defined initiatives, disciplined technical execution, and effective collaboration with stakeholders across the organisation.

This document outlines the key responsibilities and expectations of the role, but is not an exhaustive list. Responsibilities may evolve in line with business priorities, technological developments, and the organisation's analytical maturity. The role requires a high level of autonomy, the ability to think clearly in ambiguous situations, and strong personal accountability for deliverables and outcomes.

Qualifications & Experience

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or related field.
• Relevant experience in data analysis, machine learning, or data engineering.
• Strong proficiency in Python.
• Strong proficiency in SQL.
• Solid understanding of machine learning fundamentals, including feature engineering, model training, and evaluation.
• Strong analytical and structured problem-solving skills.
• Ability to independently drive analytical projects from requirement clarification to delivery.
• Strong communication and stakeholder management skills.
• Experience with PySpark.
• Experience working with large datasets or distributed environments.
• Exposure to production ML systems or data pipeline architectures.