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Tier-1 brand, mid-level generalist AI/platform role, and broad skill requirements increase candidate competition.
Core cloud and distributed systems skills are transferable, but specialized AI platform and LLM experience increases domain specificity.
Explicit 4–7 years plus mandatory cloud, microservices, and programming stack enforces strict filters.
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Design, build, and operate scalable, distributed AI platform services and microservices powering AI-driven products.
Own end-to-end delivery of complex technical features including design, deployment, operational support, and troubleshooting in cloud-native environments.
Drive architecture and design discussions; optimize performance, scalability, and reliability of AI platforms using modern tools and cloud technologies.
Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience).
4–7 years of professional software engineering experience.
Strong programming skills in Go, Python, Java, or C++ and expertise in data structures, algorithms, distributed systems, and software design.
Experience with cloud-native microservices, Kubernetes, Docker, SQL/NoSQL databases, REST APIs, cloud platforms, and CI/CD pipelines.
Experienced in large-scale distributed systems and AI platform engineering with focus on cloud-native architectures.
Proven capability to lead technical initiatives from concept to production and collaborate with cross-functional teams.
Knowledgeable about emerging AI technologies including LLMs, AI agents, RAG, with experience in observability tools and event-driven architectures.