Quick Overview
Seniority
Mid Senior
Work mode
Hybrid
Location
Woodbridge Township, NJ, United States
Posted
19 hours ago
FastAPIMicroservicesSpringSpring BootOWASPAzureC++Generative AIJavaLLMPython
Job Description
Hi,
Our client is looking for AI-Native Developer in Iselin, NJ below is the detailed requirement.
Job Title: AI-Native Developer
Location: Iselin, NJ
Job Description:
- Bachelor’s degree in related field.
- AI Native Engineering.
- 7–12+ years of overall software engineering experience.
- 3–5+ years in AI/ML engineering with at least 2 years in Generative AI and Agentic AI in production.
- Java — Enterprise applications, Spring Boot, microservices, AI SDK integration.
- C++ — Systems-level development, AI inference engine integration (ONNX, TensorRT, llama.cpp).
- Python — LLM orchestration (LangChain, LangGraph, AutoGen), data pipelines, FastAPI.
- Production experience with end-to-end AI-powered business applications.
- Proven expertise in Agentic AI architectures — tool-use, memory management, multi-agent orchestration, and human-in-the-loop workflows.
- Hands-on across AI SDLC use cases — AI-driven code review, automated test generation, intelligent QA pipelines, and AI-powered requirements analysis.
- Proficiency with LLM APIs (OpenAI, Anthropic, Azure OpenAI, or open-source equivalents), RAG pipelines, vector databases, and embedding strategies.
- Solid experience in traditional application security — OWASP Top 10, secure coding, SAST/DAST, API security, and threat modeling.
- Hands-on with AI-specific security risks — prompt injection, RAG poisoning, model data leakage, and adversarial inputs.
- Familiarity with OWASP LLM Top 10, NIST AI RMF, MITRE ATLAS.
- Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.
- Lead Automation Transformation & Provide Visibility to Business Segment & Leadership.
Nice to Have:
- AI integration with enterprise platforms (ERP, CRM, DevSecOps toolchains).
- LLM observability and evaluation (RAGAS, DeepEval, Promptfoo).
- AI governance, responsible AI, and explainability in enterprise contexts.
- Open-source contributions or published technical content.
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