AI Software Development Engineer in Test (AI-SDET)

Who We Are

At Network Solutions,we’vebeen trusted for decades to help people get online and stay ahead.We’vebeen here since the beginning of the internet, andwe’restillbuildingfor what comes next.

As the original digital identity authority, we help secure domain names, protect brands, and safeguard the infrastructure businesses rely on. We empower our customers to own and manage the assets that define them online, while delivering enterprise-grade security to protect against virtual threats. Our teamleveragesmodern, AI-accelerated tools to streamline how businesses manage their digital presence, making the most of our decades of experience.

The Network Solutions team is here to help online businesses protectwhat’stheirs and build for tomorrow.That’swhy millions trust us to protect their domains, brands, and websites every day.

Network Solutions is seeking an AI-SDET to serve as a change agent for our QA Guild, introducing AI-powered tools, evaluation frameworks, and intelligent automation that modernize how our quality engineering team works. You will validate LLM outputs, RAG pipelines, and autonomous agent workflows while coaching and upskilling QA engineers on emerging AI practices. The ideal candidate brings 4–10+ years of engineering experience with 2–4 years focused on AI/ML, strong Python skills, and a proven ability to drive process change within engineering teams.

KEY RESPONSIBILITIES

1. Change leadership and team enablement

• Champion the adoption of AI-powered testing tools and practices across the QA Guild.

• Identify gaps in current QA processes and introduce AI-driven solutions to address them, from automated test generation to self-healing frameworks.

• Run workshops, lunch-and-learns, and knowledge-sharing sessions to upskill QA engineers on AI tools and methodologies.

• Serve as the internal subject matter expert on AI quality standards, emerging tooling, and best practices.

• Collaborate with QA leads, engineering managers, and product teams to embed AI quality practices into the SDLC.

2. AI and LLM quality engineering

• Design and maintain evaluation (Evals) frameworks covering hallucination detection, groundedness, toxicity, bias, prompt regression, determinism, and safety guardrail validation.

• Build automated validation pipelines for LLM outputs using structured assertions, semantic scoring, and embedding-based metrics.

• Validate RAG architectures including chunking strategies, embedding quality, retrieval accuracy, re-ranking, and vector search relevance.

• Test multi-agent orchestration workflows, agent memory and context retention, and MCP (Model Context Protocol) integrations.

• Create synthetic and benchmark datasets for AI testing scenarios.

• Evaluate latency, token usage, model cost, throughput, and scalability of AI systems.

3. Automation engineering

• Develop and maintain scalable UI and API automation frameworks using Python, Robot Framework, and Playwright/Selenium/Cypress.

• Automate testing across REST APIs, GraphQL, event-driven systems, streaming services, webhooks, microservices, and micro-frontend (MFE) architectures.

• Build reusable automation libraries, utilities, and AI-assisted testing workflows.

• Integrate automation and quality gates into CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI/CD, Azure DevOps).

4. AI platform and agent validation

• Validate AI agents built on LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, and OpenAI SDKs.

• Work with vector databases including Pinecone, Weaviate, ChromaDB, Qdrant, and FAISS.

• Validate AI workflows involving tool calling, function calling, agent planning and execution, structured outputs, and multi-modal systems.

• Monitor AI system behavior using observability, logging, and tracing platforms.

5. DevOps, cloud, and reliability

• Work within Docker and Kubernetes environments.

• Perform cloud-based testing and deployments across AWS, Azure, OCI, and GCP.

• Validate distributed system reliability and resiliency.

• Contribute to shift-left and quality engineering best practices across the organization.

REQUIRED QUALIFICATIONS

Must have

• Strong programming experience in Python.

• Deep understanding of software testing and quality engineering principles.

• Hands-on experience with AI/LLM testing, prompt engineering, context engineering, and AI Evals.

• Practical experience with RAG systems, AI agents, and vector databases.

• Experience with CI/CD pipelines, Docker, Kubernetes, and at least one major cloud platform (AWS, Azure, OCI, or GCP).

• Experience with API testing, microservices testing, and UI automation.

• SQL proficiency and data validation experience.

• Demonstrated ability to influence engineering teams and drive adoption of new tools and processes.

• Strong analytical, debugging, and troubleshooting skills.

• Experience working in Agile/Scrum environments.

Preferred

• Experience with Robot Framework and Playwright.

• Knowledge of LangChain, LangGraph, LlamaIndex, CrewAI, or Semantic Kernel.

• Familiarity with MCP (Model Context Protocol).

• Hands-on experience with developer AI tools such as Cursor, GitHub Copilot, Claude, or Gemini.

• Exposure to MLOps workflows, performance testing, security testing, or AI governance and compliance requirements.

• Experience with contract testing, chaos testing, or resilience engineering.

• AI Red Teaming or LLM security testing experience (prompt injection, jailbreaks, data leakage).

• Familiarity with accessibility testing and cross-browser/device testing.

EDUCATION

Bachelor’s or Master’s degree in Computer Science, Engineering, AI/ML, or a related field preferred. Equivalent hands-on experience and a demonstrable portfolio of projects are highly valued.

SUCCESS PROFILE

The right candidate for this role is more than a strong engineer. We are looking for someone who:

• Actively experiments with AI tools and builds side projects, prototypes, or open-source contributions.

• Can bridge traditional quality engineering practices with modern AI-native approaches.

• Communicates clearly, influences without authority, and earns trust across engineering, product, and leadership teams.

• Brings a continuous improvement mindset and a bias toward action.

• Rapidly learns and applies emerging AI frameworks, tools, and architectures.

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