AI-Native Developer (AI-Assisted Engineering)

PeopleNTech LLC

  • Alexandria, VA
  • 2 days ago
  • $120,000–$130,000 Per Year

Highlights

Proficiency with LLM APIs (OpenAI, Anthropic, Azure OpenAI, or open-source equivalents), RAG pipelines, vector databases, and embedding strategies. We are seeking an AI Native Engineer with proven hands-on experience building enterprise business applications and agentic AI solutions in production.

Numbers & Facts

LocationAlexandria, VA
Salary$120,000–$130,000 Per Year

Description

Client Name

CLS Bank (Please do not post client name anywhere)

Job ID

187832

Indent ID

PSL-US211102_2-10

Position Title

AI-Native Developer (AI-Assisted Engineering)

Location

101 Wood Avenue South, Suite 200, Iselin, NJ 08830, USA

Employment Type

Fulltime

Rate

$120,000 to $130,000 Per Annum + 5% APB

Indent ID

TBC

Job ID

TBC

Job Description

About the Role:


We are seeking an AI Native Engineer with proven hands-on experience building enterprise business applications and agentic AI solutions in production. The role requires strong technical depth across AI-augmented software development and application security. You will work alongside an experienced in-house AI expert and are expected to contribute at a peer level.

Key Responsibilities:

  • 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.

Required Qualifications:

  • AI Native Engineering
  • 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):

  • 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.

Experience:

  • 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.

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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