Data & AI Engineer — AWS, Java & Python

Role overview A hands-on engineering position focused on building and operating AWS-based data platforms and AI-powered applications for a guest engagement product serving multi-unit restaurant brands. The role blends Java backend development with practical Python work and applied AI, spanning ingestion pipelines, modeling, generative AI features, and production operations that support personalized engagement, churn prediction, and natural-language analytics.

Responsibilities - Design and ship scalable AWS data, backend, and AI solutions with attention to reliability, security, performance, and cost. - Develop Java and Spring Boot services, APIs, and data models, integrating with MySQL, MongoDB, and external partner platforms. - Build and maintain dependable ingestion and processing pipelines across transaction, ordering, loyalty, payment, and guest data sources. - Modernize the data platform using S3, EMR, Spark, Glue, and Athena, evolving the existing lake architecture. - Produce trusted, reusable datasets and models that power analytics, customer intelligence, and machine learning use cases. - Build generative AI and retrieval-augmented applications using Amazon Bedrock, OpenSearch, and related tooling, while supporting predictive ML work in segmentation, recommendations, and churn. - Establish testing, monitoring, and evaluation standards for pipelines, AI quality, and application performance. - Uphold strong security, privacy, tenant isolation, and governance practices across customer and guest data. - Maintain engineering quality through CI/CD, infrastructure as code, automated testing, code review, and clear documentation.

Requirements - Five or more years of production software or data engineering experience with strong Java, Spring Boot, and AWS backgrounds. - Solid skills in Java, SQL, and Python, plus experience working with large-scale relational and document data stores. - Hands-on experience building AI or ML applications with technologies such as Amazon Bedrock and SageMaker, including generative AI, retrieval-augmented generation, embeddings, and model evaluation. - Demonstrated ability to design reliable data pipelines covering incremental processing, schema evolution, reconciliation, and failure recovery. - Strong grasp of AWS infrastructure, messaging, security, monitoring, and containerized application patterns. - Experience with automated testing, CI/CD, infrastructure as code, and production observability, supported by sound engineering judgment and clear communication.

Nice to have Experience with OpenSearch, streaming or change-data-capture technologies, advanced data lake architectures, predictive ML and recommendation systems, and prior work with restaurant technology, payments, loyalty, or point-of-sale integrations.