Job Description & Summary 

Drive the design, development, and deployment of enterprise-grade agentic AI solutions built on a strong foundation of Java microservices, cloud-native architecture, and secure, scalable platforms. The role blends Agentic AI engineering with modern backend engineering practices to deliver production-ready, resilient, and governed AI systems, along with the ability to contribute to end-to-end application layers. 

Responsibilities 

• Design and build agentic AI systems integrated with enterprise Java microservices (Spring Boot-based architectures). 

• Develop and deploy AI agents using frameworks such as LangChain, LangGraph, Bedrock Agents, AutoGen, or CrewAI. 

• Implement RAG pipelines, prompt engineering techniques, and evaluation frameworks. 

• Build secure, scalable APIs and backend services using Spring Boot and opinionated Internal Developer Platforms (IDP). 

• Implement OAuth 2.0, JWT-based authentication, and secure service-to-service identity. 

• Architect Kubernetes-native applications following 12-Factor principles. 

• Enable observability using OpenTelemetry (OTEL), logging, tracing, and metrics. 

• Design centralized configuration and secrets management using HashiCorp Vault or AWS Secrets Manager. 

• Implement GitOps-based deployment strategies using tools such as ArgoCD. 

• Build resilient systems using retries, circuit breakers (Resilience4j). 

• Implement API security and authorization using RBAC and ABAC models. 

• Work with service mesh technologies such as Istio. 

• Integrate cloud IAM and Kubernetes workload identity. 

• Develop event-driven systems using Apache Kafka. 

• Collaborate with teams to deliver scalable AI solutions. 

Mandatory Skill sets

• Strong Java and Spring Boot microservices development experience. 

• Experience in building cloud-native applications on Kubernetes. 

• Hands-on experience with OAuth2, JWT, and secure API development. 

• Deep knowledge of distributed systems and resilience patterns. 

• Strong experience with observability tools (OTEL, Prometheus, Grafana). 

• Strong experience with Kafka-based event-driven architecture. 

• Hands-on with GitOps, CI/CD pipelines, and container platforms. 

• Proficiency in GraphQL API design and development. 

• Understanding of GenAI fundamentals including LLMs, RAG pipelines, and agentic AI landscape. 

• Experience with ReactJS for integrated application layers. 

• Hands-on experience with NoSQL databases (MongoDB, DynamoDB, etc.). 

Preferred Skill sets

• Exposure to Internal Developer Platforms (IDP). 

• Experience with service mesh (Istio). 

• Knowledge of multi-cloud deployment (AWS/Azure/GCP). 

• Exposure to agentic UX patterns. 

• Familiarity with AI orchestration tools. 

• Hands-on experience with agentic AI systems in production. 

• Experience with tools like Claude, Cursor. 

• Deep experience with agentic frameworks (LangGraph, CrewAI, etc.). 

• Exposure to full-stack AI applications. 

Certifications (Preferred) 

AWS Developer / Solutions Architect 

Kubernetes Certification (CKA/CKAD), AWS or Azure AI Certifications