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Specialized AI role with metro location and moderate-brand consulting firm, yielding medium applicant density.
AI plus cloud and Java integration skills are transferable but require ML-specific experience, yielding medium sensitivity.
Explicit 7+ years and mandatory AI, Java and AWS Bedrock/SageMaker experience create high shortlisting strictness.
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Design, develop, and maintain AI-powered Java/Spring Boot applications integrated with AWS cloud services including Bedrock, SageMaker, and Lambda focused on RAG architecture.
Own end-to-end AI/ML feature implementation from model building to production-grade service deployment and integration with enterprise tooling such as Jira and ServiceNow.
Build and manage data pipelines, embeddings, vector and graph databases, ensuring scalable, secure, and cost-efficient infrastructure using AWS ECS Fargate, EventBridge, API Gateway, IAM, and related technologies.
7+ years of experience in software and AI engineering with domain expertise in AI/ML, RAG implementations, and cloud-native architectures.
Proficiency in Java and Spring Boot for backend development; experience with AWS services such as Bedrock, SageMaker, Lambda, Step Functions, ECS/Fargate, EventBridge, API Gateway, IAM, and Secrets Manager is mandatory.
Experience with vector databases like OpenSearch or Aurora PostgreSQL (pgvector) and graph database Amazon Neptune; knowledge of embedding pipelines and RAG architectures.
Bachelor's degree in Computer Science, Information Technology or related field; relevant hands-on experience accepted in lieu of formal degree. Certifications in AWS and Java preferred but not mandatory.
Experienced in integrating AI/ML solutions within enterprise Java-based backend systems on AWS with a strong focus on scalable, secure, and maintainable cloud architectures.
Practitioner with hands-on skills in end-to-end AI model deployment, vector search technologies, and automated pipeline orchestration using AWS Step Functions and Lambda.
Capable of collaborating cross-functionally with frontend engineers and enterprise workflow integrations, demonstrating mastery over complex distributed cloud-native systems and RAG architectures.