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Tier-1 employer and mid-seniority increase candidate density despite niche agentic AI specialization.
High: requires specialized agentic AI frameworks, multi-agent orchestration, and cloud-native SRE skills tied to ML platforms.
High: explicit 5–10 years plus mandatory cloud-native, Kubernetes, event-driven, and Python/ML platform experience.
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Design, implement, and optimize complex agent orchestration and multi-agent workflows using frameworks like LangChain, Langraph, and GoogleADK.
Lead event-driven architecture development and enhancements, maintaining high availability and reliability of runtime components.
Mentor junior engineers, drive improvements in deployment and monitoring across AWS, Google Cloud, and Azure, and contribute to platform documentation and best practices.
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
5–10 years of hands-on experience designing, building, and deploying distributed or agentic systems.
Proficient in Python and at least one additional modern language such as Java, Go, or C++.
Experience with event-driven, microservices, multi-agent architectures, cloud-native development on AWS, Google Cloud, or Azure, and familiarity with CI/CD, Kubernetes, and monitoring.
Experienced mid-career engineer comfortable leading agentic AI platform architecture and multi-cloud environment operations.
Capable of independently designing complex agent workflows and influencing adoption of agentic stack best practices.
Effective mentor with strong cross-functional collaboration and technical leadership skills.