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# GCP ML Architect

- **Company:** OpenKyber LLC
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Continuous Integration, DevOps, Github, Apache JMeter, Python (Programming Language), Load Testing, Machine Learning, Natural Language Processing, NLTK (NLP Analysis), OpenCV, Scrum Methodology, Systems Development Life Cycle, Tensorflow, Azure Machine Learning, Selenium, Strategies of Testing, TypeScript, Management of Software Versions, Google Cloud, Pytorch, Delivery Pipeline, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, ReadyAPI, Pytest, Scikit Learn, HuggingFace, Playwright, Virtual Agents, Software Coding, Data Pipelines, SDET, Jenkins
- **Published:** May 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0c9fcdbb30f98349

## About the Role

Do you have experience in TypeScript?, AI, LLMs & Agentic Systems Strong hands-on experience with LLMs, prompt engineering, RAG, vector DBs, and model evaluation. Proficiency with LangChain, HuggingFace, Transformers, OpenAI/Ollama APIs. Experience to agentic AI frameworks like LangGraph, AutoGen, CrewAI. Build and enhance GenAI-powered QE solutions, AI agents, and autonomous workflows. Implement MCP-driven, context-aware automation and CI/CD decision intelligence.
Automation Engineering Strong coding skills in Python, TypeScript, or Java. Architect and maintain automation frameworks for: oUI: Playwright, Selenium oAPI: PyTest, Requests, RestAssured oPerformance: JMeter, Locust Develop prompt-optimized, AI-generated test assets and validation mechanisms.
ML/AI Engineering & Data Pipelines 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.
Cloud, DevOps & Integration Strong understanding of AWS/Azure/Google Cloud Platform architectures and AI/ML services. Integrate automation pipelines using GitHub Actions, Azure DevOps, Jenkins. Ensure scalable, secure, and governed AI/automation environments.
Leadership & Delivery Excellence Provide technical leadership and mentor teams on AI adoption and automation best practices. Collaborate closely with developers, SMEs, and product teams to align on architecture and roadmap. Drive feature prioritization, quality strategy, and solution design. Lead defect triage, quality reviews, and compliance with QE/AI governance. Work across the full SDLC, contributing to test strategy, design, execution, and analysis. Operate effectively in an Agile/Scrum environment.

## Description

Architect / Senior SDET / AI QE Technical Lead Experience: 1014 years Role Overview 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.


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