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Our client is a leading financial institution in Hong Kong , investing significantly in AI, cloud technologies, and enterprise platform modernization. We are seeking a hands-on AI Platform Engineer to support and maintain enterprise AI platforms, data platforms, and cloud-native environments. This role focuses on production support, platform operations, data pipeline reliability, and AI platform enablement. This is an exciting opportunity to work at the intersection of AI, cloud, and platform engineering, partnering with AI Engineers, Data Engineers, and Infrastructure teams to deliver enterprise-grade AI solutions. Why Join? Join a leading financial institution driving enterprise AI adoption and innovation. Play a key role in building and supporting the foundation for next-generation AI services. Work with modern technologies including Kubernetes, CI/CD, Cloud Infrastructure, Infrastructure as Code, and AI platforms. Partner with high-caliber AI, Data, and Engineering teams on strategic transformation initiatives. Own and drive automation, platform reliability, and operational excellence across critical environments. Gain exposure to large-scale, enterprise-grade systems within a highly regulated and technology-driven organization. Excellent opportunity to develop expertise in AI platform operations, MLOps, DevOps, and cloud-native technologies. Key Responsibilities Provide Level 1 and Level 2 support for AI platforms, data platforms, and cloud-native applications Monitor and maintain production environments, ensuring platform stability and availability Support incident management, troubleshooting, root cause analysis, and problem resolution Support deployment, release management, system upgrades, and change implementation Monitor data pipelines, ETL processes, model services, and platform health Support AI/ML lifecycle operations and collaborate with Data Scientists and AI Engineers Maintain system documentation, operational procedures, runbooks, and support records Identify automation opportunities to improve operational efficiency and platform reliability Requirements 2+ years of experience in Platform Support, Application Support, Cloud Operations, MLOps, Data Engineering, or Data Platform environments Strong experience supporting production systems within enterprise environments Experience supporting cloud platforms such as AWS, Azure, or Alibaba Cloud Experience with data platforms and pipeline technologies including Airflow, Spark, PySpark, Databricks, Hive, Iceberg, or Kafka Strong troubleshooting, analytical, and communication skills
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.