Senior AI/ML Engineer – Platform Architecture

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.

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.

• 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.

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

• 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.