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Tier-1 brand, mid-level AI engineer, metro location, and broad skill requirements increase applicant competition.
Requires combined ML/AI and backend cloud expertise, making cross-industry transfer moderately constrained.
Multiple mandatory specialized skills and explicit 4–7 years requirement enforce strict candidate filters.
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Design, develop, and deploy enterprise-grade agentic AI systems integrated with Java Spring Boot microservices and cloud-native Kubernetes architectures.
Implement AI agents using frameworks like LangChain, LangGraph, Bedrock Agents, and develop secure, scalable backend APIs with OAuth2, JWT authentication, and resilience patterns.
Manage observability, centralized configuration, secure API gateways, and deployment using GitOps tools such as ArgoCD; collaborate to deliver scalable AI solutions.
4 to 7 years of relevant work experience.
Strong Java and Spring Boot microservices development skills.
Experience with Kubernetes, OAuth2, JWT, and security in API development.
Proficiency in Generative AI fundamentals, RAG pipelines, agentic AI frameworks, plus experience with Kafka, observability tools (OTEL, Prometheus).
Experienced in building cloud-native, scalable AI solutions using Java microservices within Kubernetes environments.
Demonstrated ability in implementing secure, resilient distributed backend systems employing service mesh, API security, and GitOps CI/CD workflows.
Strong familiarity with agentic AI systems, generative AI agents, and full-stack integration including ReactJS and NoSQL databases.