Design and implement agentic workflow orchestration using LangGraph, including multi-agent collaboration, tool integration, memory management, and human-in-the-loop patterns. Define and execute an AI-in-SDLC strategy embedding intelligent automation across code generation, automated testing, code review, release notes, and deployment.
Numbers & Facts
Location
Jersey City, NJ
Description
Our Client, an IT Services and Consultant company, is looking for a Full Stack AI Consultant for their Remote/Hybrid (Jersey City, NJ) location.
Responsibilities:
Assess current engineering practices and deliver a prioritized modernization roadmap with AI integration at its core.
Build Enterprise Shared Services and APIs that could be reused across different product teams
Define and execute an AI-in-SDLC strategy embedding intelligent automation across code generation, automated testing, code review, release notes, and deployment.
Design and implement agentic workflow orchestration using LangGraph, including multi-agent collaboration, tool integration, memory management, and human-in-the-loop patterns.
Build reusable reference implementations, libraries, and playbooks for AI-augmented engineering.
Drive adoption of DevOps, CI/CD, and observability practices with AI-driven enhancements.
Advise on engineering policies and standards that embed AI-first principles.
Upskill existing engineers on agentic AI patterns and AI-integrated development practices.
Requirements:
15+ years in software engineering with advisory experience.
Expertise in Java, Spring Boot, Angular, and cloud-native architectures on Microsoft Azure.
Strong background in microservices, Docker/Kubernetes, API-first design, and event-driven architectures.
Hands-on experience orchestrating agentic AI workflows using LangGraph, LangChain, or comparable frameworks
Proven ability to design multi-agent systems with tool use, planning, and RAG patterns.
Experience embedding AI into the SDLC like AI-assisted coding, intelligent test generation, automated documentation, and release automation.
Strong understanding of LLM orchestration, prompt engineering, and AI observability.