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Strong employer brand, metro location, and mid-level experience increase competition despite niche agentic AI plus Java microservices skills.
Role demands specific combination of agentic AI, Java microservices, Kubernetes, and Kafka, reducing cross-industry transferability.
Explicit 4–7 years requirement plus many mandatory tech stacks and security/cloud certifications increases filter strictness.
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Design, develop, and deploy enterprise-grade agentic AI solutions leveraging Java microservices (Spring Boot), cloud-native architecture, and scalable secure platforms.
Build and integrate AI agents using frameworks like LangChain, LangGraph, and implement RAG pipelines with prompt engineering and evaluation systems.
Implement secure APIs, Kubernetes-native applications, observability, GitOps deployments, resilient distributed systems, event-driven architecture, and cloud IAM integrations.
4 to 7 years of relevant experience in Java and Spring Boot microservices development.
Strong hands-on experience with Kubernetes, OAuth2, JWT, secure API development, distributed systems resilience, observability tools (OTEL, Prometheus, Grafana), Kafka event-driven systems, and GitOps CI/CD pipelines.
Bachelor's degree in Engineering (BE/BTech) or MCA/MTech/MBA.
Understanding of Generative AI fundamentals, agentic AI landscape, and proficiency in GraphQL API development; experience with ReactJS and NoSQL databases (MongoDB, DynamoDB).
Experienced backend engineer capable of building production-ready AI systems combining Java microservices and agentic AI frameworks.
Skilled in cloud-native Kubernetes environments implementing security, resilience, observability, and event-driven architectures.
Familiarity with advanced AI orchestration, agentic AI production tools, multi-cloud deployments, and Internal Developer Platforms (IDP) preferred.