AI QE Architect and Governance Leader

About Apexon:

Apexon is a digital-first technology services firm specializing in accelerating business transformation and delivering human-centric digital experiences. We have been meeting customers wherever they are in the digital lifecycle and helping them outperform their competition through speed and innovation.

Apexon brings together distinct core competencies in AI, analytics, app development, cloud, commerce, CX, data, Dev Ops, IoT, mobile, quality engineering and UX, and our deep expertise in BFSI, healthcare, and life sciences to help businesses capitalize on the unlimited opportunities digital offers. Our reputation is built on a comprehensive suite of engineering services, a dedication to solving clients toughest technology problems, and a commitment to continuous improvement.

Backed by Goldman Sachs Asset Management and Everstone Capital, Apexon now has a global presence of 15 offices (and 10 delivery centers) across four continents.

We enable #HumanFirstDIGITAL

AI QE Architect and Governance Leader
- Job description:

We are seeking an experienced AI Quality Engineering (QE) Architect & Governance Leader to drive end-to-end quality, automation, and governance for AI, ML, and GenAI solutions across mission-critical and highly regulated environments. This role combines deep expertise in automation, AI/ML technologies, and agentic workflows with strong leadership in governance, risk, and complianceespecially for Energy/Utilities/Nuclear domains.

You will architect future-state AI QE frameworks, establish testing standards for LLMs and agents, automate validation pipelines, enforce model governance, and lead enterprise-wide assurance initiatives.

Visa Status US

Country USA, Canada

Location Onsite FPL West Palm Beach , Florida

Core Experience & Qualifications

• 1014 years as a Technology Architect, SDET, or QE Leader with strong coding skills in Python, Type Script, 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 Lang Graph, Auto Gen, 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:

Lang Chain, Hugging Face, GPT models, vector databases

• Working knowledge of ML/DL libraries:

Scikit-learn, Tensor Flow, Keras, PyTorch, Hugging Face 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, Dev Ops & CI/CD

• Knowledge of deploying AI and automation solutions on AWS.
• Experience implementing CI/CD pipelines for ML and LLM models:

oModel versioning & lifecycle

oRetraining workflows

oAutomated evaluation gates

oInfrastructure-as…