Software Development Engineer / Engineering Software

Property Finder
London
2 months ago
Applications closed

Related Jobs

View all jobs

Software Development Engineer, S3

Software Development Engineer II, Talent Evaluation

Software Engineer (Research & Development)

Sr. System Dev. Engineer, WW AMZL Innovation and Design Engineering

Software Engineer

Software Engineer - Mandarin Speaking

Relocation would be required but with highly competitive, tax free salary package.A UAE-born startup, Property Finder expanded its operations to Qatar, Bahrain, Saudi Arabia, Egypt and Turkey over the yearsThe company is one of the largest technology start-ups in the region and a recent Unicorn.

As the VP, Engineering for Enterprise B2B Services, you will head the strategic development and execution of Property Finder’s client-facing applications and enterprise catalog services.Your responsibilities include overseeing engineering managers and technical leads to deliver high-quality software adhering to modern architectural standards.Champion collaboration across product management, design, and engineering teams to develop market-leading enterprise services and data products.Propel the integration of AI technologies within product and engineering teams to foster innovation and enhance product offerings.Mentor and guide engineering managers and technical leaders, enhancing team productivity, engagement, and performance.Direct initiatives for the modernization of legacy systems and accelerate the delivery of new product capabilities within B2B services and data solutions.Oversee critical domains such as Enterprise Catalog Services, Client-Facing B2B Applications, Agent Experience, and Agent Onboarding Platforms, to ensure optimal performance and user satisfaction.Manage the deployment of web applications across more than five countries in the MENA region, customizing solutions to meet diverse local compliance and business needs.Implement stringent engineering processes and governance throughout the product development lifecycle to guarantee the delivery of high-quality releases.Define project timelines and oversee execution strategies in close collaboration with product management.Cultivate an environment that attracts, develops, and retains elite engineering talent while promoting an inclusive workplace culture that encourages innovation and professional growth.Promote a culture of quality, speed, and excellence in operational practices within the engineering teams, leveraging metrics for continuous improvement.Minimum of 15 years in engineering leadership, managing expansive, geographically dispersed software engineering teams.Demonstrated success in architecting and scaling exceptional engineering organizations.Deep understanding of contemporary software engineering practices, architectural norms, and team dynamics.Strong background in data products and AI technology landscapes.Comprehensive experience overseeing the entire software development lifecycle of SaaS products.

Proficiency in data-driven product development.Knowledge in machine learning and cutting-edge technologies.Well-versed in agile software development methodologies.In-depth understanding of security, privacy, and compliance within SaaS ecosystems.Go, PHP, Python, Swift UI, Kotlin, React, AWS, and Kubernetes.

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Leading UK Machine Learning Labs and Institutes: Pioneering the Future of AI

Over the past decade, machine learning (ML) has transformed from an academic field into a linchpin of commercial innovation. From personalised product recommendations to healthcare diagnostics, it fuels the algorithms behind every major breakthrough in the digital world. For data-driven enterprises and researchers, the United Kingdom offers a vibrant landscape of top-tier ML labs, academic institutes, and forward-looking industries. If you’re aiming to forge a career in this exciting domain—be it in cutting-edge research, industry applications, or policy-making—this comprehensive guide, written for MachineLearningJobs.co.uk, will walk you through the leading UK machine learning hubs. We’ll delve into the opportunities they present, the latest research challenges they tackle, and the career paths that await those ready to shape the future of AI.

Shadowing and Mentorship in Machine Learning: Gaining Experience Before Your First Full-Time Role

How to Find Mentors, Build Industry Connections, and Hone Your Technical & Soft Skills for a Thriving ML Career Machine learning (ML) is transforming industries at a rapid pace, fuelling breakthroughs in healthcare, finance, e-commerce, manufacturing, cybersecurity, and beyond. As the demand for ML expertise skyrockets, the competition for early-career opportunities has intensified. It’s not enough to complete an online course or a university degree; employers are looking for proven, hands-on experience. So, how do you stand out from the crowd? Two powerful strategies—shadowing and mentorship—can help you bridge the gap between academic theory and industry practice. By learning directly from experienced professionals, you gain practical insights and build the confidence needed to excel in real-world ML roles. In this in-depth guide, we’ll explore why mentorship is crucial, how to find the right mentors (both formally and informally), how to demonstrate your value as a mentee, and the best ways to shadow machine learning practitioners. By the end, you’ll be equipped with the knowledge and tactics to jump-start your career and secure your first full-time ML role.

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.