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VBEST Software Inc · New York, NY

Java Developer with Agentic AI

$145,600 – $156,000 / yearPosted 24 days ago
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Stack mentioned

javaagentic-aiawskubernetesapache-kafkanosqlci/cdsplunktest-automation

Tasks

Skills required - Java, AWS preferred, Kubernetes, some streaming/Kafka,

NOSQL, CI/CD, testing, alerts, and monitoring(Splunk/dynatrace).

Must know AI for code optimization to accelerate development.

Locations: NYC NY/ Jersey City NJ

W2 Candidates only

Only locals

Requirements

Skills required - Java, AWS preferred, Kubernetes, some streaming/Kafka,

NOSQL, CI/CD, testing, alerts, and monitoring(Splunk/dynatrace).

Must know AI for code optimization to accelerate development.

- Develops secure and high-quality production code, and reviews and debugs code written by others

- Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.

- Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.

- Drives decisions that influence the product design, application functionality, and technical operations and processes

- Serves as a function-wide subject matter expert in one or more areas of focus

- Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle

- Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.

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