AI / Java Architect

AI - Java ArchitectFull time onlyAtlanta, GADescription:We are seeking an experienced Developer III with strong solution engineering expertise to deliver scalable, cloud-native data and AI-driven solutions. This role requires hands-on ownership across the full software development lifecycle (analysis, design, development, and testing), with a clear expectation that testing and quality engineering are core and mandatory responsibilities.The ideal candidate will bring deep expertise in Java/ Python, AWS services (Redshift, RDS, GLUE, DMS, ECS, Bedrock), and Generative AI, with a strong ability to design and implement robust, production-ready systems. Experience with Apache Spark and GraphQL APIs is a strong plus.Key ResponsibilitiesSolution Engineering & Analysis (Required)Lead requirements analysis by collaborating with business stakeholders, product managers, and technical teamsTranslate business needs into end-to-end scalable architecture and solution designsDesign cloud-native systems leveraging AWS services (Redshift, RDS, DMS, ECS, Bedrock)Evaluate solution trade-offs around performance, scalability, cost, and maintainabilityProduce detailed technical specifications, architecture diagrams, and design documentationDevelopment (Required)Design, develop, and maintain backend services and applications using:Java (Spring Boot, microservices)Python (APIs, data processing, GenAI integration)Build and optimize data pipelines and data access layers using Redshift and RDSDevelop event-driven and containerized applications using ECSDevelop GenAI-enabled applications using AWS Bedrock, including LLM-based workflows and RAG patternsTesting & Quality Engineering (Mandatory Core Responsibility)Own testing as an integral part of development—not a separate functionDesign and implement comprehensive test strategies, including:Unit testing, Integration testing, End-to-end/system testing, Regression testingBuild and maintain automated test frameworks and integrate with CI/CD pipelinesValidate: Data accuracy across Redshift/RDS pipelines, Performance and scalability of servicesReliability and correctness of GenAI outputsActively debug, troubleshoot, and resolve defects, ensuring production-grade qualityData Engineering & ProcessingDesign and manage structured and semi-structured data workflows in Redshift and RDSImplement efficient data ingestion and transformation strategies(Preferred) Develop large-scale distributed data processing using Apache Spark (PySpark/Scala)GenAI & AI IntegrationBuild and deploy applications leveraging Generative AI (LLMs via AWS Bedrock)Implement: Prompt engineering strategiesRetrieval-Augmented Generation (RAG) architecturesAgentic workflowsEnsure responsible AI practices including performance tuning, monitoring, and governanceCollaboration & DeliveryWork in a fast-paced Agile environment, contributing to sprint planning and reviewsCollaborate across engineering, data, and product teamsProvide technical leadership and mentorship where neededSupport production deployments and ongoing system enhancementsRequired Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, or related field5+ years of experience in software engineering and solution designStrong expertise in:Java (Spring Boot, microservices architecture), Python (data engineering, APIs, AI/ML integration)Hands-on experience with AWS services, including:Amazon RedshiftAmazon RDSAWS DMS (Database Migration Service)Amazon ECS (containerized workloads)Amazon Bedrock (GenAI services)Proven experience owning end-to-end SDLC (analysis, development, testing)Strong understanding of distributed systems and cloud-native architecturesPreferred Qualifications: Experience with Apache Spark (PySpark or Scala Spark)Experience designing and implementing GraphQL APIsKnowledge of CI/CD pipelines, DevOps practices, and containerization (Docker/Kubernetes)Experience with vector databases and GenAI ecosystem toolsKey Skills: Strong solution engineering and system design capabilities, Ability to independently own analysis → development → testing lifecycle, Strong focus on quality engineering and test-driven development (TDD)Excellent problem-solving, debugging, and performance optimization skillsStrong communication and stakeholder collaboration