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Recognizable multinational, metro location, and mid-senior role present moderate competition; niche infra skills limit applicants.
Platform, Kubernetes, and CI/CD skills transfer across industries, though scientific-instrument domain adds some bias.
Explicit 6–10 years plus many mandatory infra, language, and platform skills creates stringent filtering.
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Develop and maintain AI platform software components using Python and C++ to support lifecycle of AI models from training to inference.
Build and optimize backend services, containerized applications, and microservices; manage deployments on Kubernetes and ensure system reliability.
Collaborate cross-functionally within Agile Scrum/SAFe frameworks to integrate ML models, automate CI/CD pipelines, and troubleshoot complex software and infrastructure issues.
6-10 years of experience in software development or platform engineering.
Strong programming skills in Python and hands-on experience in C++.
Experience with OS-level virtualization (e.g., KVM, QEMU, Hyper-V, VMware), Docker containerization, and Kubernetes orchestration.
Bachelor's degree in computer science, electronics, instrumentation, or related technical field.
Experienced in building scalable software platforms for AI/ML model lifecycle management and cloud-native services.
Proficient in microservices architecture, automation frameworks, and Linux environments with strong troubleshooting skills.
Has worked in regulated or enterprise environments, preferably with scientific or industrial software, and familiar with Agile Scrum/SAFe processes.