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Senior Java Developer with Agentic AI

Transcend IT SolutionsNew York, NY🇺🇸United StatesPosted 17 Aug 2026

Why This Role Stands Out

This hybrid Senior Java Developer role offers a unique opportunity to pioneer AI-driven development practices, significantly impacting code quality and delivery speed within a reputable tech company. You'll thrive here if you're passionate about leveraging cutting-edge AI tools to optimize the software development lifecycle and eager to grow your expertise in agentic AI and cloud technologies. Embrace this chance to shape the future of development and advance your career in an innovative environment.

Quick Overview

Work Type
Hybrid
Level
Mid Senior

Job Description

Position Tittle: Senior Java Developer with Agentic AI
Location: NYC NY/ Jersey City NJ
 
Duration: Long Term
 
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

Skills

AWS
Splunk
Java
Kafka
Kubernetes

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