AI/ML Engineer
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AI/ML Engineer
Location
Zinkworks HQ - Athlone
Experience
Senior
Posted
Jul 10, 2026
Apply by
August 9, 2026
Applicants
0
Early applicantFull-timeHybrid
Job Description
##### **About Zinkworks**
Zinkworks 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.
##### **About the Role**
We are building a small, empowered product discovery team focused on Zinkworks’ Autonomous Networks product portfolio. This team will take new product ideas from concept through to high-fidelity working prototypes that telco operators can see, test, and react to.
As our **Senior Agentic AI Engineer**, you will own the agentic AI execution layer for the team.
This is a hands-on implementation role. You will design agent architectures, build evaluation frameworks, manage prompts and context, and ship agent-driven features to demoable, client-ready quality. The focus is not academic research. It is about applying proven agentic AI patterns quickly, thoughtfully, and to a high engineering standard.
You will work closely with backend, frontend, AI, and solution architecture colleagues to turn product ideas into working agentic systems that can be tested, demonstrated, and evolved.
##### **What You’ll Do**
- Build and ship agentic AI layers for new product prototypes.
- Design agent architectures that are reliable, explainable, and fit for purpose.
- Define and manage evaluation frameworks to measure agent quality and performance.
- Own prompt, context, and model selection decisions across the team’s prototypes.
- Select the right model, tool, or approach for each use case and clearly justify the choice.
- Partner with the Senior Backend Engineer on the boundary between agents, APIs, services, and data flows.
- Work with the Senior UI Engineer to explore agentic and post-GUI user experience patterns.
- Iterate quickly based on feedback from internal stakeholders, technical experts, and customer-facing teams.
- Feed practical learnings back into Zinkworks’ AI and Solution Architecture teams to support product strategy and roadmap decisions.
- Maintain a strong engineering quality bar while working in a fast-moving product discovery environment.
##### **What We’re Looking For**
We are looking for a senior engineer with a strong track record of building agentic AI systems in production or near-production environments.
You should be comfortable designing, implementing, testing, and improving AI agents that can interact with tools, systems, APIs, and users. You will understand the trade-offs between prompt engineering, retrieval, fine-tuning, stronger model selection, and traditional software logic.
You are an implementer first. You know how to take modern AI capabilities and turn them into working software that is reliable enough to demonstrate, evaluate, and improve.
##### **Key Skills and Experience**
- Strong Python engineering experience.
- Proven experience building agentic AI systems in production or near-production settings.
- Strong understanding of agent architectures, tool-using agents, and multi-step AI workflows.
- Experience designing or implementing evaluation frameworks for LLM or agentic systems.
- Strong prompt engineering, context engineering, and model selection experience.
- Experience working with cloud-based AI services, ideally on GCP.
- Familiarity with Vertex AI and modern LLM tooling across different providers.
- Ability to work closely with backend engineers on agent/server integration.
- Strong engineering judgement and the ability to choose simple, practical solutions over unnecessary complexity.
- Comfortable working in a fast-moving product discovery or prototyping environment.
##### **Nice to Have**
- Experience with Google ADK.
- Experience building multi-agent systems.
- Experience with tool-calling agents and agent orchestration.
- Exposure to telecoms, network operations, OSS, or autonomous networks.
- Experience contributing to AI product strategy through practical delivery learnings.
##### **Tech You’ll Work With**
- Python
- Google ADK
- GCP
- Vertex AI
- Modern LLM tooling across multiple providers
- Agent evaluation frameworks
- Prompt and context management approaches
- APIs, data services, and cloud-native backend systems
We are deliberately pragmatic about model and tooling choices. Fitness for purpose matters more than loyalty to any one provider, framework, or architecture.
##### **Inclusion & Diversity at Zinkworks**
At 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. Our commitment extends beyond the workplace, as we actively contribute to creating a more inclusive and connected society for all.
Key Responsibilities
- Build and ship agentic AI layers for new product prototypes.
- Design reliable and explainable agent architectures.
- Define and manage evaluation frameworks for agent quality and performance.
- Own prompt, context, and model selection decisions.
- Partner with backend and UI engineers on agent integration and user experience.
- Iterate quickly based on feedback from stakeholders and customers.
- Feed practical learnings back into AI and Solution Architecture teams.
Skills Required
PythonAgentic AI systemsAgent architecturesTool-using agentsMulti-step AI workflowsEvaluation frameworksPrompt engineeringContext engineeringModel selectionGCPVertex AILLM toolingAPIsCloud-native backend systemsEngineering judgementPragmatismCollaborationAdaptabilityGoogle ADKMulti-agent systemsTool-calling agentsAgent orchestrationTelecoms domain knowledgeOSSAutonomous networks
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