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Tier-1 employer brand increases applicant density despite senior, specialized AI-platform requirements.
Requires deep distributed systems, cloud-native, and AI-platform experience, limiting cross-industry transferability.
Explicit 8–12 years requirement plus mandatory cloud, Kubernetes, and programming skills.
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Design, develop, deploy, and maintain highly scalable AI platform services and distributed microservices using languages like Go, Python, Java, or C++.
Own end-to-end delivery of complex AI platform product features including design, production deployment, operational support, and troubleshooting of distributed cloud-native systems.
Lead technical design reviews, mentor junior engineers, drive engineering best practices, and collaborate with cross-functional teams to ensure scalable, reliable, secure, and maintainable AI platform solutions.
8-12 years of professional software engineering experience with strong expertise in data structures, algorithms, distributed systems, and software design principles.
Bachelor's or Master's degree in Computer Science, Engineering, or related field, or equivalent experience.
Strong programming skills in Go, Python, Java, or C++ with hands-on experience building cloud-native microservices and distributed systems.
Experience with Kubernetes, Docker, containerized development, SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB, Redis, Cassandra), RESTful APIs, and familiarity with cloud platforms like AWS, Azure, or GCP.
Experienced in building and operating large-scale distributed AI platforms with a focus on scalable, resilient, and secure cloud-native services.
Demonstrated technical leadership with ability to drive architectural discussions, lead technical initiatives end-to-end, and mentor junior engineers effectively.
Familiar with emerging AI technologies such as Large Language Models (LLMs), AI Agents, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and hands-on experience with CI/CD pipelines and automated testing workflows.