Zinkworks is a global leader in innovation, headquartered in Athlone, Ireland, with three office locations worldwide. We utilise the latest cutting-edge technologies to bring industry-leading expertise to our Telecommunication and Financial Services clients. We are adept at developing custom innovations that streamline our clients’ workflows and improve operational efficiency. With a commitment to quality and customer satisfaction, we have earned a reputation as a trusted partner for businesses seeking reliable software services.
Role Overview
We are seeking a Senior AI/ML Engineer to lead the design and delivery of the machine learning core that powers an AI app platform. 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. 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.
The ideal candidate will bring deep expertise in MLOps, neural network development, and a track record of delivering AI solutions into production environments. Your work will directly contribute to enabling a no-code AI ecosystem through modular, plug-and-play ML components.
Key Responsibilities
* Design and implement reusable, production-grade ML modules (e.g., neural networks, regression, classification) for integration into a no-code rApp platform.
* Lead MLOps strategy: establish scalable model lifecycle management including versioning, deployment pipelines, and monitoring.
* Build seamless model training workflows integrated with telecom PM counters, KPIs, and pre-processing logic.
* Drive the creation of training pipelines, evaluation frameworks, and hyperparameter tuning mechanisms with clear metrics and iteration tracking.
* Develop and enforce robust data ingestion and pre-processing pipelines, ensuring clean, real-time, and reproducible data for model training and inference.
* Support integration of trained models into closed-loop automation systems, enabling intelligent network functions.
* Collaborate with DevOps, Backend, and UX teams to expose ML capabilities through intuitive interfaces and microservices.
* Champion best practices in AI/ML development—testing, explainability, observability, and governance.
Required Skills and Experience
Must-Haves:
* 5+ years of experience in AI/ML engineering with a strong emphasis on end-to-end delivery into production environments.
* 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).
* Deep knowledge of neural networks and traditional ML models (e.g., time-series, classification, regression) with demonstrated applications in production settings.
* High proficiency in Python, with experience building scalable, API-driven microservices.
* Experience deploying containerised ML services using Docker, with strong familiarity with CI/CD workflows.
* Expertise in model validation, tuning, metadata tracking, and training history.
Nice-to-Haves:
* Experience with no-code/low-code ML platforms or visual ML tooling.
* Strong stakeholder management and interpersonal skills.
* Exposure to telecom domain standards, including O-RAN or PM/KPI systems.
* Familiarity with Kafka, AVRO, and stream processing for real-time AI use cases.
* Experience integrating AI pipelines into observability and monitoring platforms.
* Previous involvement in closed-loop automation projects or intelligent rApp systems.
* Experience within the Telecomms industry
What We Offer
* A pivotal role in building next-generation AI solutions for the telecom industry, with opportunities to influence platform direction and lead innovation.
* Collaboration with a highly motivated, forward-thinking team committed to pushing the boundaries of what AI can do in telecom.
* Competitive compensation including health benefits, retirement plans, and performance bonuses.
* A culture of inclusivity, growth, and technological excellence—where your contributions directly shape real-world systems.
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