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Java Developer with Agentic AI

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Onsite

Java Developer with Agentic AI

VBest Software Inc

Java Developer with Agentic AI

Skills

JavaKafkaSplunkAIAWSKubernetes

2 months ago

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Important information


Contract type:

Freelance / Contract

Salary:

$140,000 – $150,000 / year

Location:

New York, NY, USA

Work mode:

Onsite

Published on:

17 August 2026

What they need


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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