Sr. Java Developer with good GenAI Experience

Sr. Java Developer with good GenAI Experience Location : Charlotte NC - Fully onsite Job Description: We are seeking a Cloud Java GenAI Engineer to build and scale highly distributed, data-intensive applications and embed Generative AI capabilities into enterprise platforms. The role requires strong hands-on engineering in Java 8/J2EE, Spring Boot microservices, Spring Batch, Kafka streaming, and Cloud (preferably Azure), along with proven experience delivering GenAI solutions (RAG/LLM-based APIs, agentic workflows, and production-grade guardrails).Key ResponsibilitiesDesign and develop highly scalable, distributed data applications in cloud environments.Build and maintain Java 8/J2EE backend services with strong expertise in Java Concurrency, IO, Collections, multi-threading, and performance tuning.Develop microservices using Spring Framework / Spring Boot, following clean architecture and API-first design.Implement and optimise Spring Batch jobs for large-scale data processing and scheduled workloads.Design and develop enterprise-grade APIs using REST API design and/or SOAP Web Services as required.Implement messaging and event-driven solutions using Kafka streaming (must-have), including topic design, consumer groups, ordering, retries, and DLQ patterns.Build robust persistence layers using JPA, SQL optimisation, indexing strategies, and efficient query design.Work in a CI/CD/CT setup with automated testing, pipeline-based deployments, and quality gates (e.g., Sonar).Ensure secure engineering practices: handle security vulnerabilities, secrets management, dependency hygiene, and secure coding standards.Implement and operate systems in production using observability and monitoring tooling (logs, metrics, traces).GenAI Responsibilities - Should have Knowledge Build and integrate Generative AI features into applications (e.g., chat, summarisation, Q&A, recommendations, automation assistants).Implement RAG (Retrieval-Augmented Generation) patterns: chunking, embeddings, retrieval, reranking (if applicable), grounding, and response validation.Integrate with LLM providers/services on cloud (e.g., Azure OpenAI preferred; AWS Bedrock / GCP Vertex AI acceptable depending on platform).Build GenAI services as secure, scalable APIs with latency, cost, and reliability considerations.Implement guardrails: prompt controls, content filtering, PII redaction, evaluation metrics, and monitoring for hallucination/drift.Enable and support agentic/automation workflows where applicable (tool/function calling, multi-step orchestration).Must-Have Skills / QualificationsStrong experience building distributed, highly scalable systems and data applications.Strong hands-on expertise in:Java 8 / J2EEJava Concurrency, IO, Collections, Multi-threadingJPA Framework, database query optimisationREST API design / SOAP Web ServicesStrong experience with Spring Framework, Spring Boot, Microservices architecture.Spring Batch hands-on experience (must).Strong experience in Kafka streaming / messaging (Good Kafka is must).Strong SQL skills; deep understanding of relational data (critical).Experience working in CI/CD/CT with automated unit tests, build pipelines, and deployment automation.Strong understanding of engineering processes/tools: Sonar, Jenkins, GitHub, TDD, security vulnerability management.Strong experience with at least one cloud platform: Azure / AWS / GCP (Azure preferred).GenAI experience is mandatory (LLM/RAG-based application development in production or near-production environments).Preferred / Good-to-Have SkillsObservability frameworks: Splunk / Grafana / Kibana.Containerisation & orchestration: Docker, Kubernetes.Unix/Linux proficiency and at least one scripting language (e.g., Bash, Python).Data warehousing exposure: DWH / Data Mart concepts and implementations.Hadoop ecosystem familiarity (HDFS, Hive, Spark, etc.).Strong technical/data platform knowledge across modern data stacks.Experience with AEP / AJO (Adobe Experience Platform / Adobe Journey Optimizer) – strong understanding preferred; AEP knowledge good-to-have.Front-end exposure: React preferred (for full-stack collaboration / UI integrations)."