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Specialized AI+AWS skillset but mid-tier employer and non-remote increases competition to medium.
Deep ML/RAG and AWS integration expertise create strong domain bias, so background fit sensitivity is high.
Explicit 7+ years and many mandatory AI, Java, and AWS technologies make shortlisting highly strict.
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Architect, develop, test, and maintain AI-powered Java/Spring Boot applications integrated with AWS cloud services.
Design and build robust AI/ML data pipelines, embeddings, and RAG (Retrieval-Augmented Generation) mechanisms using AWS services like Bedrock, SageMaker, Lambda, and vector/graph databases.
Collaborate with cross-functional teams and integrate enterprise tools (Jira, ServiceNow, qTest) to deliver scalable, secure, production-grade AI solutions.
7+ years of experience in software/AI engineering, including AI/ML, RAG implementations, and cloud-native architectures.
Proficient in Java and Spring Boot backend development, with strong AWS expertise (Bedrock, SageMaker, Lambda, Step Functions, ECS/Fargate, EventBridge, API Gateway, IAM, Secrets Manager, S3).
Experience with vector databases (OpenSearch or Aurora PostgreSQL with pgvector) and graph databases (Amazon Neptune).
Bachelor's degree in Computer Science, IT, or related field; AWS certifications and advanced degrees preferred but not mandatory.
Experienced in end-to-end AI solution delivery combining ML models, embeddings, and enterprise-grade service integration on AWS cloud.
Comfortable working in cross-functional teams involving back-end (Java/Spring Boot), front-end (basic Angular), QA, and platform teams.
Demonstrates strong technical leadership in designing scalable AI pipelines, secure cloud architectures, and reliable production deployment with monitoring.