Intermediate Cloud / AI Developer

The Intermediate Cloud / AI Developer designs, develops, tests, and supports Java-based, cloud-native applications on the Google Cloud Platform (Google Cloud Platform) that enable and improve business workflows.  This role builds secure, scalable services with increasing independence, contributes to technical design and implementation decisions, and collaborates closely with DevOps, Quality Assurance (QA), Product Management, and security partners to deliver reliable solutions that meet performance, security, and compliance expectations.

 

 

 

MEANINGFUL WORK AND PERSONAL IMPACT:

 

Design, develop, and maintain Java-based applications for cloud environments, using modern engineering practices and cloud-native patterns.

Build and enhance cloud-based solutions on Google Cloud Platform that support data ingestion, processing, storage, retrieval, and distribution; contribute to solution design for reliability and performance.

Develop and maintain Representational State Transfer (REST) application programming interfaces (APIs), including versioning, documentation, backward compatibility, and integration with internal and external systems.

Implement AI-enabled features by integrating approved AI services or models into applications; contribute to basic evaluation and monitoring approaches to ensure quality and responsible use.

Independently troubleshoot and resolve application issues across environments, perform root-cause analysis, and implement preventative fixes to reduce repeat incidents.

Apply secure coding practices and ensure solutions meet security and compliance requirements (for example: authentication/authorization, encryption in transit and at rest, audit logging, and policy-aligned data handling).

Contribute to continuous integration/continuous delivery (CI/CD) pipelines and deployment automation in partnership with DevOps to improve release quality and repeatability.

Implement and maintain observability practices including structured logging, metrics, dashboards, alerts, and operational documentation/runbooks.

Collaborate with cross-functional partners to plan and deliver releases, support testing, and resolve production issues as needed.

Participate in and contribute to architecture/design discussions and code reviews; help promote team standards and mentor junior developers through guidance and example.

 

 

WHAT YOU’LL NEED TO SUCCEED

Bring your expertise and drive for innovation to GDIT.

 

Education:

 

Bachelor’s degree in a relevant field from an accredited College/University

Alternative Path: If the candidate does not possess a relevant four-year degree, an additional four years of relevant work experience will be required.

Required Skills:

 

Hands-on experience working with cloud services and deploying applications on Google Cloud Platform (or equivalent cloud experience with the ability to ramp quickly).

Experience designing and implementing RESTful APIs, including common authentication/authorization approaches and integration patterns.

Experience integrating AI capabilities into applications (for example: calling AI APIs/services for summarization, extraction, classification, or decision support) and familiarity with basic evaluation/monitoring concepts.

Practical knowledge of secure engineering fundamentals (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 supporting applications with observability tools.

Strong collaboration and communication skills across engineering, QA, DevOps, product, and security stakeholders.

 

 

Preferred Qualifications:

 

Experience working in Agile teams and applying Software Development Life Cycle (SDLC) practices.

Familiarity with change/configuration management tools (for example: VersionOne, ServiceNow) and/or Application Lifecycle Management (ALM) practices.

Experience with container and/or serverless concepts (for example: Docker, Kubernetes fundamentals, or managed/serverless deployments).

Experience participating in production support rotations and contributing to post-incident analysis and corrective actions.