Senior Machine Learning Backend Engineer

About the Role

We are seeking a Senior Machine Learning Backend Engineer to join a partner company based in Saudi Arabia. This role focuses on developing and enhancing the machine learning infrastructure powering advanced property intelligence solutions. The engineer will integrate backend development skills with machine learning operations to build scalable platforms for model training, evaluation, deployment, and monitoring.

Collaboration is a key element, working closely with machine learning engineers, researchers, and software teams to transition models from research prototypes into dependable production environments. Responsibilities include utilising cloud-native technologies, distributed computing, automation, observability, and practicing responsible AI.

Primary Responsibilities

• Architect, maintain, and improve scalable ML infrastructures that support the entire model lifecycle including training, testing, deployment, and monitoring.
• Create effective and cost-efficient ML platforms, pipelines, and tools for handling extensive computational workloads.
• Collaborate tightly with ML engineers and researchers to convert experimental models into production-ready systems.
• Implement automation, testing frameworks, observability, monitoring, reproducibility, data tracking, and governance within ML environments.
• Assess and incorporate new data sources, tools, platforms, and technologies to boost model performance and operational efficiency.
• Work alongside software engineering, technology teams, product managers, and commercial stakeholders to deliver scalable ML solutions.
• Utilize AI-powered development tools, coding assistants, and large language model-based agents to streamline and enhance engineering processes.
• Ensure ML systems comply with security protocols, governance standards, responsible AI practices, and risk management procedures.
• Analyze technical trade-offs and contribute to architectural decisions focusing on scalability, reliability, performance, and cost optimization.

Required Qualifications and Skills

• Proven senior-level experience in backend software development combined with a solid grasp of machine learning concepts and a desire to deepen expertise in ML engineering.
• Hands-on experience designing, building, and managing ML infrastructure, platforms, and tooling for large-scale model training and deployment.
• Proficient in Python and familiar with contemporary ML engineering tools including deep learning frameworks, experiment tracking systems, version control, containerization, and automated workflows.
• Experienced in MLOps methodologies such as continuous integration and delivery, model monitoring, reproducibility, data lineage, governance, and operational management.
• Skilled with cloud-native technologies, Kubernetes, distributed computing frameworks, and scalable infrastructure supporting ML workloads.
• Understanding of artificial intelligence fundamentals and practical knowledge in applying AI tools and LLM-based coding assistants for engineering efficiency.
• Experience implementing AI-driven business solutions while adhering to ethical and responsible AI principles.
• Strong analytical capabilities, with excellent problem-solving and ability to communicate complex technical subjects to both technical and non-technical audiences.
• Proven ability to work collaboratively within cross-functional teams including ML engineers, researchers, developers, product teams, and business stakeholders.
• Preferred qualifications include a PhD in STEM disciplines; however, candidates with a Master's degree plus substantial industry experience or a Bachelor's degree with extensive relevant experience are also considered.
• Non-traditional and equivalent practical backgrounds are welcome to apply.

Benefits and Work Environment

• Engagement with cutting-edge machine learning, computer vision, geospatial analytics, and AI challenges.
• Exposure to large-scale aerial and satellite imagery technologies supporting property intelligence.
• Opportunity to work with modern cloud-native infrastructure, distributed computing, ML platforms, and AI-assisted engineering tools.
• Chance to contribute to initiatives around responsible AI, model governance, security, and risk management.
• Collaborative atmosphere involving diverse teams such as ML engineers, researchers, developers, and product experts.
• Professional development prospects through tackling complex and impactful ML infrastructure issues.
• Inclusive culture emphasizing curiosity, diversity of perspectives, integrity, teamwork, and continuous improvement.
• Applicants who may not meet every requirement but demonstrate relevant skills and experience are encouraged to apply.