Senior Java Developer with Agentic AI (less experience is also fine)

Corevia Staffing (Alaska, USA) Follow 12 hours ago

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Note: We have 10 roles here, pls pace up the submissions

Senior Java Developer with Agentic AI (less experience is also fine)

Locations: NYC NY/ Jersey City NJ

W2 Candidates only

Only locals

$80 pr hr on W2

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.

Below is the reference string to get the agentic candidates :

(LangChain OR LangGraph OR CrewAI OR AutoGen OR LlamaIndex OR LangChain4j OR LangGraph4j OR SpringAI OR "Spring AI" OR "Multi-Agents" OR MultiAgents OR "Multi Agent")

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.