Senior Machine Learning Backend Engineer

About the Role

This opportunity is for a Senior ML Backend Engineer stationed in the United Arab Emirates, working with a partner company focused on cutting-edge property intelligence. The role centers on creating and enhancing machine learning infrastructure to support large-scale model training, evaluation, deployment, and monitoring. The engineer will collaborate closely with machine learning researchers, engineers, and software teams to transition experimental models into robust production-grade platforms. This position involves working with cloud-native tools, distributed computing, automation techniques, and responsible AI principles to power solutions that analyze extensive aerial and satellite imagery for assessing climate and economic risks.

Key Responsibilities

• Develop, sustain, and improve scalable ML infrastructure covering model development pipelines, training workflows, evaluations, deployments, and operational monitoring.
• Design cost-effective, dependable ML pipelines and engineering tools capable of handling large data workloads efficiently.
• Work hand-in-hand with ML researchers and engineers to advance prototype models into scalable production systems.
• Implement automation, comprehensive testing, enhanced observability, reproducibility, data lineage tracking, and governance across all ML environments.
• Explore and incorporate new data sources, technologies, platforms, and instruments to boost model accuracy and efficiency.
• Collaborate with cross-functional teams including software developers, product managers, and commercial teams to implement scalable ML solutions.
• Utilize AI-driven development utilities, coding assistants, and large language models to optimize workflow automation and engineering productivity.
• Ensure systems conform to security standards, governance policies, responsible AI ethics, and model risk management requirements.
• Assess technical trade-offs and contribute to architectural decisions related to scalability, reliability, performance optimization, and cost considerations.

Candidate Requirements

• Extensive backend software engineering experience with a profound understanding of machine learning and enthusiasm for deepening ML technical expertise.
• Proven ability in designing, constructing, and maintaining machine learning platforms and tooling servicing large-scale model processes.
• Advanced skills in Python programming and familiarity with contemporary ML engineering tools such as deep learning frameworks, containerization, version control, and experiment tracking.
• Practical knowledge of MLOps including continuous integration/deployment, model monitoring, reproducibility, data lineage, governance, and production lifecycle operations.
• Experience using cloud-native environments, Kubernetes orchestration, and distributed computing infrastructures tailored to ML workloads.
• Strong understanding of artificial intelligence concepts with hands-on application of AI tools, coding assistants, and LLM agents for enhancing engineering tasks.
• History of implementing AI solutions addressing practical business cases in alignment with ethical and responsible AI guidelines.
• Excellent analytical and problem-solving skills along with effective communication abilities to convey technical details to diverse audiences.
• Ability to work collaboratively in multidisciplinary teams involving ML engineers, software developers, product specialists, and business stakeholders.
• Preferred qualifications include a PhD in STEM; however, a Master’s degree coupled with extensive industry experience or a Bachelor’s degree with substantial hands-on expertise is acceptable.
• Non-traditional career paths and equivalent practical experience are welcomed.

Benefits and Work Culture

• Engagement with advanced machine learning, computer vision, geospatial analytics, and AI challenges.
• Access to and experience with large-scale aerial and satellite image datasets supporting property intelligence.
• Utilization of modern cloud-native infrastructure, distributed computing platforms, and AI-powered engineering tools.
• Opportunity to participate actively in responsible AI practices, including model governance, security, and risk management.
• A collaborative atmosphere involving diverse teams of ML engineers, researchers, software developers, and product managers.
• Prospects for professional development through challenging, impactful ML infrastructure projects.
• An inclusive workplace encouraging curiosity, diversity, integrity, teamwork, and continuous learning.
• Applicants who do not meet every criterion but demonstrate relevant skills and experience are encouraged to apply.