{"id":2630922,"url":"https://alion.io/job/zinkworks-senior-aiml-engineer","title":"Senior AI/ML Engineer","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":"A","score":89,"open_postings":12,"ghost_share":0,"stale_share":0.25,"repost_share":0,"time_to_fill_p50_days":61,"computed_at":"2026-10-22T06:10:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","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":{"min_usd":86000,"max_usd":173000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":9},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"CI/CD","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Jira","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Time Series","optional":false},{"name":"Vertex AI","optional":false},{"name":"RAG","optional":true}],"status":"live","first_seen_at":"2026-10-15T13:02:47Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-23T02:25:58Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"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 Project\nWe're forming a dedicated delivery squad for a high-growth international technology client. The goal is to move customer support from reactive to proactive. The new platform will detect service issues from the client's operational data, assess their business impact, trigger remediation, and let affected customers know before they have to ask.\nIt's a greenfield, data- and AI-driven build on Google Cloud, delivered in phases. It starts with incident detection and human-approved remediation, then adds more granular detection and broader automated actions, and ends with a closed-loop system that acts autonomously within defined safety limits. You'll join a squad of around nine people, including a Solution Architect, data, ML, backend, frontend, UX, QA and platform engineers, working in the client's own tooling (Jira, Git, CI/CD).\nThe Role\nYou'll own the intelligence at the heart of the platform: the models that detect service outages and degradation and assess how serious they are for the business. You'll start with anomaly detection on operational telemetry and carry the work through to production MLOps and, later in the programme, an LLM-based agent with retrieval and tool use.\nWhat You'll Do\nExplore historical telemetry and operational data, assemble labels and engineer features, including the baselines that detection depends on.\n\nBuild a statistical baseline detector alongside an ML anomaly model, then evaluate, threshold and tune both against shadow-mode output before they go live.\n\nServe models in near real time and instrument them with model monitoring and shadow-mode tracking.\n\nDesign and train models that detect partial service degradation at a granular level, judged against expected performance rather than fixed thresholds.\n\nUpgrade the initial outage detector to a supervised model using labels generated in production.\n\nBuild MLOps foundations: a model registry, retraining triggers, champion/challenger comparison, drift detection and automated evaluation, which are the preconditions for letting models act without human review.\n\nDevelop an internal AI agent with retrieval, tool use and guardrails, supported by an evaluation harness, red-teaming and an explicit refusal policy.\n\nWork closely with data engineering on feature pipelines and with backend engineering on how model output feeds impact scoring and remediation.\n\nWhat You'll Bring\nSeveral years of hands-on experience taking ML models from exploration to production.\n\nStrong Python and the core data science stack (pandas, NumPy, scikit-learn or similar).\n\nPractical experience with anomaly detection or time-series modelling, including thresholding and dealing with noisy or sparse labels.\n\nSolid understanding of model evaluation, monitoring and drift in a live environment.\n\nExperience with MLOps tooling and practices: model registries, experiment tracking, automated retraining and CI/CD for models.\n\nExperience on a major cloud platform, ideally Google Cloud (e.g. Vertex AI, BigQuery).\n\nThe ability to explain model behaviour and trade-offs clearly\n\nNice to Have\nExperience building LLM applications with retrieval-augmented generation, tool use and guardrails, plus evaluation and red-teaming of those systems.\n\nBackground in telecoms, networking or large-scale operational data.\n\nExperience with shadow deployment, canary rollout or champion/challenger testing of models.\n\nFamiliarity with geospatial or hierarchical data.\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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