Role & seniority: AI QE Architect; 10–14 years as Technology Architect, SDET, or QE Leader.

Stack / tools: Python, TypeScript, or Java; automation frameworks; LangChain, Hugging Face, GPT models, vector databases; ML libs (scikit-learn, TensorFlow, Keras, PyTorch, HF Transformers, OpenCV, NLTK, BART); LLMs, prompt engineering, safety eval; agent frameworks (LangGraph, AutoGen, CrewAI); MCP; AWS; CI/CD; IaC; Selenium, Playwright, PyTest; API testing; K6/JMeter (nice-to-have).

Top 3 responsibilities

Design and implement automation frameworks and developer tools for large engineering orgs; build AI/GenAI solutions and agent/multi-agent workflows.
Define governance, testing standards, and observability for AI/ML systems (drift, prompts, monitoring, data lineage, model versioning).
Lead end-to-end QE efforts across functional/API/integration/perf/security/AI validation; oversee CI/CD, retraining workflows, and model evaluation gates.

Must-have skills

Strong coding in Python/TypeScript/Java; hands-on automation framework design; LLM prompt engineering and safety evaluation; agent/orchestration experience; MCP usage; ML/GenAI development; API testing; QE tooling (PyTest, Selenium, Playwright).

Nice-to-haves

Experience in regulated industries (Energy/Utilities/Nuclear/Healthcare/BFSI); RAG/embeddings; vector DBs; performance/load testing tools; AI governance, data security/privacy, drift alerts; IRB/compliance exposure.
Location & work type: not spe

Full Description

AI QE Architect

Core Experience & Qualifications 10–14 years as a Technology Architect, SDET, or QE Leader with strong coding skills in Python, TypeScript, or Java. Proven experience designing automation frameworks or developer tools for large engineering organizations. Hands-on expertise with Large Language Models (LLMs), prompt engineering, and safety evaluation techniques. Exposure to Agentic AI systems and orchestration tools such as LangGraph, AutoGen, CrewAI, or similar agent frameworks. Experience implementing Model Context Protocol (MCP) for real-time automation, autonomous workflows, or CI/CD integrations. Experience working in highly regulated industries—Energy, Utilities, Nuclear, Healthcare, BFSI, or similar. AI / ML Technologies

Practical experience with frameworks and ecosystems

LangChain, Hugging Face, GPT models, vector databases

Working knowledge of ML/DL libraries

Scikit-learn, TensorFlow, Keras, PyTorch, HuggingFace Transformers, OpenCV, NLTK and BART
Understanding of RAG architectures, embeddings, and semantic search (bonus).
GenAI & AI Agent Development
Expertise in designing, developing, validating, and deploying Generative AI solutions.
Experience building AI agents, multi-agent workflows, or autonomous decision systems.

Ability to define governance for

oLLM drift detection
oPrompt quality standards
oAgent monitoring & observability
oData lineage & model versioning
Automation & Quality Engineering
Strong experience building test automation frameworks using Python, PyTest, Selenium, Playwright, and Requests.

Ability to create automated tests covering

oFunctional
oAPI
oIntegration
oPerformance
oSecurity
oAI/ML validation (LLM testing, model accuracy, hallucination detection)
Proficiency in API testing and validation of RESTful services.

Plus: Experience with performance/load testing tools K6 or JMeter. AI Governance & Compliance Establish AI/ML quality standards, testing guidelines, and risk controls.

Define governance around

oData security & privacy
oModel evaluation KPIs (accuracy, bias, toxicity, hallucination rates)
oRegulatory alignment for Energy/Utility operations
oContinuous monitoring & drift alerts
Experience working with IRB, compliance, or audit teams.
Cloud, DevOps & CI/CD

Quality Assurance Architect