AI-Native Developer (AI-Assisted Engineering)

Job Title: AI-Native Developer (AI-Assisted Engineering)

Location: Iselin, NJ

Job Description:

• Bachelor's degree in Computer Science, Information Technology, Engineering, or related field experience with 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.
• Design and deliver production-grade business applications with AI natively integrated into the architecture.
• Build and operate Agentic AI systems — multi-step, autonomous pipelines using frameworks such as LangGraph, AutoGen, CrewAI, or equivalent.
• Drive AI SDLC practices across the development lifecycle — AI-assisted code generation, review, testing, documentation, and CI/CD augmentation.
• Apply application security expertise in both traditional and AI-specific contexts.
• Evaluate and integrate emerging AI tools and frameworks with sound engineering judgment.
• 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.

Application Security:

• 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.

Programming Languages (Minimum one, ideally two):

• Python — LLM orchestration (LangChain, LangGraph, AutoGen), data pipelines, FastAPI.
• Java — Enterprise applications, Spring Boot, microservices, AI SDK integration.
• C++ — Systems-level development, AI inference engine integration (ONNX, TensorRT, llama.cpp).

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.