At Network Solutions, we’ve been trusted for decades to help people get online and stay ahead. We’ve been here since the beginning of the internet, and we’re still building for 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 team leverages modern, 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 protect what’s theirs and build for tomorrow. That’s why 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 adecuado 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.
- 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 d

📌 AI QA Automation Engineer (Argentina)
🏢 Newfold Digital
📍 Argentina