AI QE Architect

Apexon

Bethesda (MD)

On-site

USD 150,000 - 190,000

Full time

14 days+

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Job summary

Apexon is seeking an AI QE Architect to lead design and development of next‑gen AI-powered quality engineering platforms. You will steer strategy, design scalable QE automation frameworks, and guide teams in deploying AI agents, RAG pipelines, and MCP-enabled intelligent workflows.

The role requires deep expertise in automation engineering, LLMs, and enterprise AI tooling, with hands-on work in LangChain, PyTorch, CI/CD for ML, and cloud AI services.

Qualifications

  • Hands-on experience with LLMs, prompt engineering, RAG, vector DBs, and model evaluation.
  • Proficiency with LangChain, HuggingFace, Transformers, OpenAI/Ollama API.
  • Experience with agentic AI frameworks like LangGraph, AutoGen, CrewAI.
  • Build and enhance GenAI-powered QE solutions, AI agents, and autonomous workflows.
  • Strong coding skills in Python, TypeScript, or Java.
  • Architect and maintain automation frameworks for UI: Playwright, Selenium.
  • Experience with PyTest, Requests, RestAssured.
  • Performance: JMeter, Locust.
  • Develop prompt-optimized AI-generated test assets and validation mechanisms.
  • Experience with PyTorch, TensorFlow, Scikit-Learn, NLP/CV libraries (NLTK, BART, OpenCV).
  • Build data/embedding pipelines and optimize retrieval for RAG.
  • Implement CI/CD for ML models, including versioning, evaluation, and retraining workflows.
  • Strong understanding of AWS/Azure/GCP architectures and AI/ML services.

Responsibilities

  • Lead design, development, and optimization of AI-powered QE platforms.
  • Define strategy, design scalable frameworks, and guide teams in QE automation and AI agents.
  • Drive innovation across RAG pipelines and MCP-enabled intelligent workflows.
  • Lead defect triage, quality reviews, and ensure QE/AI governance.
  • Collaborate with developers, SMEs, and product teams to align on architecture and roadmap.

Skills

LLMs
Prompt engineering
RAG
Vector databases
Model evaluation
LangChain
HuggingFace
Transformers
OpenAI/Ollama API
LangGraph
AutoGen
CrewAI
Python
TypeScript
Java
Playwright
Selenium
PyTest
Requests
RestAssured
JMeter
Locust
PyTorch
TensorFlow
Scikit-Learn
NLP/CV libraries
NLTK
OpenCV
Data/embedding pipelines
CI/CD for ML models
AWS
Azure
GCP

Tools

LangChain

Job description

The AI QE Architect will lead the design, development, and optimization of next-generation AI-powered quality engineering solutions, including platforms. This role combines deep technical expertise in automation engineering with hands-on experience in LLMs, agentic AI frameworks, and enterprise-grade AI tooling. The architect will define strategy, design scalable frameworks, guide teams, and drive innovation across QE automation, AI agents, RAG pipelines, and MCP-enabled intelligent workflows

Key Skills & Responsibiliti

esAI, LLMs & Agentic Syste

  • msStrong hands-on experience with LLMs, prompt engineering, RAG, vector DBs, and model evaluatio
  • n.Proficiency with LangChain, HuggingFace, Transformers, OpenAI/Ollama API
  • s.Experience to agentic AI frameworks like LangGraph, AutoGen, CrewA
  • I.Build and enhance GenAI-powered QE solutions, AI agents, and autonomous workflow

e.
Automation Engineer

  • ingStrong coding skills in Python, TypeScript, or Ja
  • va.Architect and maintain automation frameworks f

or:oUI: Playwright, Selen

iumoAPI: PyTest, Requests, RestAssu

redoPerformance: JMeter, Loc

  • ustDevelop prompt-optimized, AI-generated test assets and validation mechanis
  • inesExperience with PyTorch, TensorFlow, Scikit-Learn, NLP/CV libraries (NLTK, BART, Open
  • CV).Build data/embedding pipelines and optimize retrieval for
  • RAG.Implement CI/CD for ML models, including versioning, evaluation, and retraining workfl

ows.
Cloud, DevOps & Integr

  • ationStrong understanding of AWS/Azure/GCP architectures and AI/ML serv
  • kins.Ensure scalable, secure, and governed AI/automation environm

ents.
Leadership & Delivery Exce

  • llenceProvide technical leadership and mentor teams on AI adoption and automation best prac
  • tices.Collaborate closely with developers, SMEs, and product teams to align on architecture and ro
  • admap.Drive feature prioritization, quality strategy, and solution d
  • esign.Lead defect triage, quality reviews, and compliance with QE/AI gover
  • nance.Work across the full SDLC, contributing to test strategy, design, execution, and ana
  • lysis.Operate effectively in an Agile/Scrum enviro
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