Role & seniority
- AI QE Architect; 6–10 years as SDET or Sr QE with strong coding skills
Stack/tools
- Languages: Python, TypeScript, Java
- Automation: PyTest, Selenium, Playwright, Requests; API testing
- AI/ML: LangChain, Hugging Face, GPT models, vector databases; scikit-learn, TensorFlow, Keras, PyTorch, Transformers, OpenCV, NLTK, BART
- LLM/Agent: Large Language Models, prompt engineering, agent frameworks (LangGraph, AutoGen, CrewAI); MCP (Model Context Protocol)
- Architecture/DevOps: CI/CD for ML/LLM; AWS; Infrastructure-as-Code; observability
- Other: RAG, embeddings, semantic search (bonus)
Top 3 responsibilities
- Design, build, and maintain automation frameworks and developer tools for large engineering organizations; implement MCP for real-time automation, autonomous workflows, or CI/CD integrations
- Design, validate, and deploy Generative AI solutions; build AI agents/multi-agent workflows; establish governance (drift detection, prompt quality, agent monitoring, data/model versioning)
- Define AI governance and compliance standards; align with data security/privacy, regulatory needs, IRB/compliance interactions; lead testing strategy across functional/API/integration/AI-ML validation; ensure observability and risk-based quality
Must-have skills
- 6–10 years SDET/SQE experience; strong coding skills; proven automation framework or developer tool design
- Hands-on with LLMs, prompt engineering, safety evaluation; experience w

Full Description

AI QE Architect

Core Experience & Qualifications 6-10 years as a SDET, or Sr QE 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
Knowledge of deploying AI and automation solutions on AWS.

Senior Quality Assurance Specialist