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Tier-1 brand, generic software/devops title, mid-level experience and metro location drive high candidate competition.
MLOps and Kubernetes skills transfer across industries, though enterprise AI and finance exposure increase domain specificity.
Mandatory 3+ years, Python, Docker/Kubernetes skills and CKAD certification increase strictness.
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Builds, deploys, and maintains AI/ML platform infrastructure including deployment pipelines, containers, and infrastructure-as-code.
Implements monitoring, logging, alerting, and security controls to ensure reliable and secure AI/ML production services.
Supports release automation, environment management, and participates in on-call production support and incident response.
3+ years of applied software engineering experience with formal training or certification.
Proficiency in Python and familiarity with modern software engineering practices such as testing, version control, and code review.
Experience with containerization (Docker), Kubernetes concepts, and certified in Kubernetes (e.g., CKAD or similar).
Hands-on experience using enterprise-authorized AI-assisted software development tools, with ability to critically evaluate AI-generated outputs.
Experienced in building and operating cloud-native AI/ML platform infrastructure with focus on reliability, security, and observability.
Skilled in integrating AI-assisted development tools responsibly within engineering workflows, ensuring data security and correct output validation.
Familiar with agile processes, production support models, and engaging in collaborative code reviews and technical discussions in a regulated environment.