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Property intelligence is reshaping how the world understands the built environment, and Nearmap is driving that. We put powerful aerial imagery, AI-driven analytics, and geospatial tools into the hands of the people who plan, build, insure, and govern the places we all live and work. Our technology turns property uncertainty into decisive action, and our culture brings out the best in the people who build it. We move fast, we care about craft, and we're proud of what we're building. If you're energized by turning hard problems into real-world impact, we'd love to meet you. Job Descripti on Execute software engineering tasks to support end-to-end machine learning operations, with a focus on scaling workflows for large data and distributed systems Design, build, and maintain MLOps systems, including microservices, queuing systems, APIs, and orchestration workflows using Python, Kubernetes, Kafka, and modern database systems. Implement observability tools such as Prometheus and Grafana to ensure reliability, performance, and visibility of ML systems in production Collaborate closely with data scientists and machine learning engineers to streamline workflows for LLM, generative AI, and Agentic AI development and deployment. Review architecture and implementation plans to ensure alignment with organizational goals, scalability, and best practices. Mentor junior and mid-level engineers, fostering a culture of collaboration, innovation, and operational excellence Qualifications Education & Exper ience Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field. 5+ years of professional experience in MLOps, DevOps, or Software Engineering, with a focus on building scalable and reliable software systems. Core Technical Ex pertise Proficiency in Python and Linux, with strong knowledge of designing scalable, distributed systems. Hands-on experience in designing, implementing, and maintaining MLOps workflows, including CI/CD pipelines, monitoring, and production optimization. Strong background in cloud computing (AWS/GCP), infrastructure as code (Terraform), containerization, and orchestration (Kubernetes). Solid understanding of modern software development practices such as test-driven development (TDD), systems thinking, and CI/CD automation. Observability & Re liability Experience deploying and managing observability tools such as Prometheus, Grafana, and OpenTelemetry to ensure high reliability and performance in production ML systems. Expertise in scaling and optimizing distributed systems for large-scale, multi-node computations. What we offer: Medical care Sport Card (MultiSport) MultiLife (mental and physical wellbeing) Attractive employee referral program Nearmap subscription (naturally) B2B or UoP
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.