Artificial Intelligence Engineer

Role: Senior AI Engineer

Type: 9 -Month Day Rate Contract (with potential for extension)

Location: Remote (occasional on-site presence required in Dublin)

I am looking for a Senior AI/ML Engineer to lead the development of the machine learning core for a cutting-edge AI app platform. Your mission will be to design and implement modular ML components that power a no-code, drag-and-drop experience—enabling users with little ML experience to build intelligent, automated applications (rApps).

What You’ll Do:

• Architect and implement scalable, reusable ML components (e.g., regression, classification, deep learning) for integration into a no-code rApp builder.
• Build and optimize end-to-end model pipelines—from data ingestion and preprocessing to training, evaluation, and deployment.
• Enable seamless drag-and-drop model configuration and training in collaboration with UI/UX and frontend teams.
• Integrate training workflows with telecom data (PM counters, KPIs), and support transformations.
• Manage the full ML lifecycle, including versioning, tuning, and metadata tracking.
• Ensure clean, timely, and robust data pipelines for model training.
• Support real-time and closed-loop automation through ML model deployment.
• Promote best practices in testing, monitoring, explainability, and MLOps.
• Work cross-functionally with DevOps, backend, and UX teams to surface ML functionality via intuitive interfaces.

Must-Have:

• 5+ years of experience in AI/ML engineering, ideally within platform or product development.
• Hands-on experience with ML lifecycle management and MLOps practices (preferably on GCP).
• Strong background in building and validating time-series, regression, or classification models.
• Deep Python expertise, plus experience with microservices and RESTful APIs.
• Cloud-native ML experience, especially with GCP tools like Vertex AI, BigQuery ML, or AI Platform.
• Experience with Docker and CI/CD-based model deployment.

Nice-to-Have:

• Experience with no-code/low-code ML environments.
• Telecom industry knowledge, including O-RAN standards or PM/KPI systems.
• Familiarity with real-time data tools such as Kafka and AVRO.
• Experience integrating ML into observability/monitoring platforms.
• Understanding of closed-loop automation in operational systems.