Machine Learning Architect Role
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Zinkworks is a global leader in innovation, headquartered in Athlone, Ireland, with three office locations worldwide.
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We utilise the latest cutting-edge technologies to bring industry-leading expertise to our Telecommunication and Financial Services clients.
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We are adept at developing custom innovations that streamline our clients' workflows and improve operational efficiency.
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With a commitment to quality and customer satisfaction, we have earned a reputation as a trusted partner for businesses seeking reliable software services.
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Job Description
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We are seeking a highly skilled Machine Learning Architect to lead the design and delivery of the machine learning core that powers an AI app platform.
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This is a high-impact role focused on architecting a scalable system that enables users to build robust, AI-powered applications with minimal ML background.
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You will own the entire ML lifecycle—from data ingestion to model deployment—and will be critical in ensuring the system is production-ready, secure, and adaptable.
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Key Responsibilities
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1. Design and implement reusable, production-grade ML modules (e.g., neural networks, regression, classification) for integration into a no-code rApp platform.
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2. Lead MLOps strategy: establish scalable model lifecycle management including versioning, deployment pipelines, and monitoring.
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3. Build seamless model training workflows integrated with telecom PM counters, KPIs, and pre-processing logic.
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4. Drive the creation of training pipelines, evaluation frameworks, and hyperparameter tuning mechanisms with clear metrics and iteration tracking.
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5. Develop and enforce robust data ingestion and pre-processing pipelines, ensuring clean, real-time, and reproducible data for model training and inference.
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6. Support integration of trained models into closed-loop automation systems, enabling intelligent network functions.
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7. Collaborate with DevOps, Backend, and UX teams to expose ML capabilities through intuitive interfaces and microservices.
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8. Champion best practices in AI/ML development—testing, explainability, observability, and governance.
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9. Must-Haves
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10. 5+ years of experience in AI/ML engineering with a strong emphasis on end-to-end delivery into production environments.
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11. Proven hands-on experience in MLOps, including automated pipelines, CI/CD integration, model serving, and monitoring (preferably on Google Cloud Platform using tools like Vertex AI, BigQuery ML).
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12. Deep knowledge of neural networks and traditional ML models (e.g., time-series, classification, regression) with demonstrated applications in production settings.
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13. High proficiency in Python, with experience building scalable, API-driven microservices.
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14. Experience deploying containerised ML services using Docker, with strong familiarity with CI/CD workflows.
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15. Expertise in model validation, tuning, metadata tracking, and training history.
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16. Benefits
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17. A pivotal role in building next-generation AI solutions for the telecom industry, with opportunities to influence platform direction and lead innovation.
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18. Collaboration with a highly motivated, forward-thinking team committed to pushing the boundaries of what AI can do in telecom.
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19. Competitive compensation including health benefits, retirement plans, and performance bonuses.
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20. A culture of inclusivity, growth, and technological excellence—where your contributions directly shape real-world systems.
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