{"id":3484982,"url":"https://alion.io/job/zinkworks-test-lead","title":"Test Lead","company":{"id":694430,"name":"Zinkworks","domain":"zinkworks.com","url":"https://alion.io/company/zinkworks","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Teamtailor","truth_index":{"grade":"B","score":84,"open_postings":17,"ghost_share":0.176,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":61,"computed_at":"2026-11-01T06:10:25Z"}},"role":"QA","role_family":"QA","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Athlone, Ireland"],"countries":["IE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Apache Kafka","optional":false},{"name":"CI/CD","optional":false},{"name":"DeepEval","optional":false},{"name":"Digital Twin","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Langfuse","optional":false},{"name":"LangSmith","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Promptfoo","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Ragas","optional":false},{"name":"Rest API","optional":false},{"name":"CrewAI","optional":true},{"name":"Google ADK","optional":true},{"name":"Google Cloud Run","optional":true},{"name":"Google GKE","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-10-29T17:08:43Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-11-01T15:47:20Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":2},"description":"About Zinkworks\nZinkworks partners with leading Telecommunications and Financial Services organizations to modernize legacy systems, migrate mission-critical platforms to the cloud, and engineer AI-driven automation. From OSS transformation to rApp development and network intelligence, our teams simplify complexity and turn it into competitive advantage. Based in Ireland and operating across the EU, UK, and US, Zinkworks combines deep domain expertise with delivery excellence to help clients modernize faster and operate smarter.\nAbout the Role\nWe are looking for a hands-on QA Lead to own quality for our Network Digital Twin and agentic AI use cases in the telecoms and broadband domain. You will define how we test AI agents that reason, call tools and act on live network data, and you will build and run that testing yourself.\nThis is not a strategy-only role. You will set the QA strategy for agentic applications, then write the evaluation harnesses, automation and test scenarios that put it into practice. You will work alongside AI engineers, data scientists, developers and architects on cloud-native systems running on GCP.\nYou will bring a solid software testing background and extend it into the newer discipline of agentic testing: non-deterministic outputs, LLM evaluation, tool-calling correctness, guardrails and safety. Familiarity with telecom/PON is a strong bonus.\nWhat You'll Do\nDefine the QA strategy for agentic Software systems\n\nOwn the end-to-end QA strategy for agentic applications, covering agents, tools, prompts, RAG pipelines, multi-agent workflows and the Digital Twin services they act on.\n\nDefine quality gates and acceptance criteria for non-deterministic systems: task success rate, tool-call accuracy, groundedness, hallucination rate, latency and cost per task.\n\nSet the approach for offline evaluation, pre-release regression and production monitoring of agent behaviour.\n\nDefine how the Digital Twin is validated for fidelity against the real network, and how it is used as a safe sandbox to test agents before they touch live systems.\n\nBuild and run the testing, hands-on\n\nBuild and maintain evaluation harnesses and golden datasets for agent scenarios, including LLM-as-judge, rubric-based and deterministic assertions.\n\nTest tool and API calls made by agents (including MCP and function calling) for correctness, sequencing, error handling and permission boundaries.\n\nDesign adversarial and safety tests: prompt injection, jailbreaks, data leakage, unsafe or out-of-scope actions, and guardrail bypass.\n\nWrite automated tests for REST and event-driven microservices behind the Digital Twin and integrate them into CI/CD pipelines.\n\nRun exploratory and scenario-based testing of agent workflows, including human-in-the-loop approvals and failure recovery.\n\nDebug agent behaviour using traces, logs and observability tools (e.g. LangSmith, Langfuse, OpenTelemetry, Cloud Logging) to find the root cause of failures.\n\nLead quality across the team\n\nLead and mentor QA engineers, and coach developers and AI engineers on testing practices for AI systems.\n\nTrack and report quality metrics and model/prompt regressions to engineering and product leadership.\n\nWork with product owners and architects to make agents testable by design, with clear specifications, observability and evaluation hooks.\n\nTake part in agile ceremonies and drive continuous improvement in how we test AI.\n\nWhat You'll Bring\n7+ years in software testing, with at least 2 years leading QA for a team or product area.\n\nA strong foundation in testing distributed microservices, REST APIs and event-driven systems (e.g. Kafka, Pub/Sub).\n\nHands-on experience testing LLM-based or agentic applications, including evaluation of non-deterministic outputs.\n\nPractical knowledge of agent evaluation methods: golden datasets, LLM-as-judge, trajectory and tool-call evaluation, and regression across model or prompt changes.\n\nExperience with at least one evaluation or observability framework such as DeepEval, Ragas, promptfoo, LangSmith or Langfuse.\n\nSolid coding skills in Python (or similar) for building test automation and evaluation pipelines, with tools such as Pytest.\n\nUnderstanding of AI risks and how to test for them: hallucination, prompt injection, bias, data leakage and unsafe actions.\n\nExperience embedding automated tests and quality gates into CI/CD pipelines.\n\nAbility to debug backend and agent interactions using logs, traces and metrics.\n\nClear communication, with the ability to explain AI quality risks to technical and non-technical stakeholders.\n\nNice to Have\nExperience testing Digital Twins, simulators or network emulation environments.\n\nFamiliarity with the telecom/PON domain (Passive Optical Networks) and network operations use cases.\n\nExperience on Google Cloud Platform, including Vertex AI, GKE, Pub/Sub or Cloud Run.\n\nKnowledge of agent frameworks such as LangGraph, Google ADK, CrewAI or the Model Context Protocol (MCP).\n\nPerformance and load testing of AI services, including token cost and latency budgets.\n\nInclusion & Diversity at Zinkworks\nAt Zinkworks, we are deeply committed to fostering a culture of diversity, inclusion, and belonging. We believe that our strength lies in the unique backgrounds, perspectives, and experiences of our team members. By embracing an inclusive environment, we empower innovation and collaboration across all levels of our organisation. Based in Ireland, we are proud to support and engage with our local communities through meaningful initiatives that promote equity and opportunity. 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