Intermediate Cloud/AI Developer

Responsibilities

• Design, develop, and maintain Java-based applications for cloud environments using modern engineering practices and cloud-native patterns.
• Build and enhance GCP solutions supporting data ingestion, processing, storage, retrieval, and distribution, with a focus on reliability and performance.
• Develop and maintain REST APIs, including versioning, documentation, backward compatibility, and integrations with internal and external systems.
• Implement AI-enabled features by integrating approved AI services or models and contribute to evaluation and monitoring approaches.
• Troubleshoot application issues across environments, perform root-cause analysis, and implement preventative fixes.
• Apply secure coding practices, including authentication/authorization, encryption, audit logging, and policy-aligned data handling.
• Contribute to CI/CD pipelines and deployment automation in collaboration with DevOps.
• Implement and maintain observability practices, including structured logging, metrics, dashboards, alerts, and operational documentation/runbooks.
• Collaborate with engineering, QA, DevOps, Product Management, and security teams to plan releases, support testing, and resolve production issues.
• Participate in architecture/design discussions and code reviews and provide guidance to junior developers.

Requirements

• Bachelor's degree in a relevant field from an accredited college/university.
• Candidates without a relevant four-year degree must have an additional four years of relevant work experience.
• 3+ years of experience developing backend applications or services in Java.
• Hands-on experience with cloud services and deploying applications on GCP, or equivalent cloud experience with the ability to ramp up quickly.
• Experience designing and implementing RESTful APIs, including authentication/authorization approaches and integration patterns.
• Experience integrating AI capabilities into applications, such as AI services/APIs for summarization, extraction, classification, or decision support.
• Familiarity with basic AI evaluation and monitoring concepts.
• Practical knowledge of secure engineering fundamentals, including least privilege access, secrets management, encryption, and audit logging.
• Proficiency with Git-based workflows and working knowledge of CI/CD concepts and release practices.
• Strong troubleshooting skills, including defect triage, performance tuning, and application support using observability tools.
• Strong collaboration and communication skills across engineering, QA, DevOps, product, and security stakeholders.
• Able to obtain and maintain a Public Trust and complete onsite fingerprinting at a designated facility.

Preferred Qualifications

• Experience working in Agile teams and applying Software Development Life Cycle (SDLC) practices.
• Familiarity with change/configuration management tools such as VersionOne and ServiceNow and/or Application Lifecycle Management (ALM) practices.
• Experience with container and/or serverless concepts, including Docker, Kubernetes fundamentals, or managed/serverless deployments.
• Experience participating in production support rotations and contributing to post-incident analysis and corrective actions.