Lead/Senior Backend Java Developer (Agentic AI)

Quick Facts

• Role: Lead/Senior Backend Java Developer with Agentic AI

• Contract: 6 months (contract-to-hire)

• Openings: New York, NY (3), Jersey City, NJ (2), Wilmington, DE (1)

Description

Lead backend engineering for secure, scalable production services using Java, Spring Boot, Kafka, and AWS, with full SDLC ownership. Drive enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes. Build and evaluate agentic AI workflows with robust state management and failure handling.

Responsibilities

• Execute creative software solutions—design, development, and technical troubleshooting with the ability to think beyond routine approaches.
• Develop secure, high-quality production code; review and debug code written by others.
• Drive adoption of AI-assisted engineering practices (e.g., AI-assisted code review/refactoring, accelerated test strategy, incident/root-cause analysis support).
• Establish consistent validation standards: secure coding, peer review, and automated testing; promote reusable patterns.
• Leverage SDLC toolchain capabilities (including AI-assisted development/automation) to improve value realized through automation.
• Identify opportunities to eliminate or automate remediation of recurring issues to improve operational stability.
• Lead evaluation sessions with external vendors, startups, and internal teams to probe architectural designs and technical fit.

Requirements

• Advanced Core Java expertise, including concurrency, collections, JVM behavior, and error handling.
• Strong Spring Boot and API development skills: contract design, validation, authentication, exception handling, and versioning.
• Kafka expertise: partitioning, offsets, consumer groups, duplicate processing, retries, and message ordering.
• System design ability for scalable microservice-based applications, including bottleneck and trade-off reasoning.
• AWS proficiency: real service usage, architecture, deployment, monitoring, IAM, and troubleshooting.
• Resiliency knowledge: timeouts, retries with backoff, circuit breakers, idempotency, graceful degradation.
• Performance engineering experience: latency, throughput, connection pools, database indexes, caching, load testing.
• Agentic AI experience: agent workflows, tools, state management, failure handling, and evaluation.
• Technical leadership in Agile environments, including mentoring, code review, architecture ownership, and production decision-making.
• Strong understanding of CI/CD, continuous delivery, TDD, cloud development, resiliency, and security.

Benefits

• Contract-to-hire employment model.
• Influence and shape AI-assisted engineering practices and engineering standards.