We are seeking a hands‑on Machine Learning Engineer to design, build, and deploy production‑grade AI systems, with a strong focus on NLP and modern transformer‑based models.
This role goes beyond model development—you will own end‑to‑end ML systems, from problem formulation and data pipelines to deployment, monitoring, and continuous improvement. You’ll play a key role in embedding AI into mission‑critical systems (e.g., healthcare platforms), ensuring solutions are scalable, reliable, and compliant.
This is an ideal role for someone who combines deep ML expertise (especially NLP/LLMs) with strong MLOps and software engineering skills.
Key Responsibilities
End‑to‑End ML Ownership
Design and implement training pipelines, evaluation frameworks, and inference systems
Collaborate closely with product, data, and backend engineering teams to deliver real‑world impact
Build and optimize NLP systems using transformer architectures (e.g., BERT, encoder–decoder, decoder‑only models)
Fine‑tune and adapt pretrained language models for domain‑specific use cases
Evaluate model performance, trade‑offs, and metrics for NLP tasks
* (Bonus) Work with large‑scale models using GPU clusters, data/model parallelism, or quantization techniques
MLOps & Production Systems
Build and maintain CI/CD pipelines for ML systems
Deploy models as scalable APIs and microservices
Monitor model performance, data drift, and system health in production
Implement robust versioning, testing, and rollback strategies
Develop and optimize ETL pipelines and data workflows (e.g., healthcare formats like FHIR/HL7 where applicable)
Build and maintain feature stores and data layers for training/serving consistency
Integrate ML outputs into production systems alongside backend teams
Engineering Excellence & Infrastructure
Write clean, maintainable, production‑grade Python code
Use Docker and Kubernetes to orchestrate ML workloads
Work with cloud platforms (AWS, Azure, or GCP) for scalable infrastructure
Ensure systems meet security, privacy, and compliance standards (e.g., regulated environments like healthcare)
Requirements
Experience & Seniority
4+ years of professional experience in machine learning, data engineering, or software engineering
Proven track record of hands‑on coding and system building (not just leadership or oversight)
Experience working on production ML systems, not only research or prototypes
Advanced Python proficiency (primary language)
Experience with ML frameworks such as PyTorch or TensorFlow
Ability to build training pipelines, evaluation workflows, and inference systems
Experience with production‑grade engineering (testing, scalability, deployment)
NLP & Transformers
Hands‑on experience with transformer models (e.g., BERT, RoBERTa, encoder–decoder, decoder‑only)
Experience fine‑tuning pretrained models
Strong understanding of NLP evaluation metrics and performance trade‑offs
MLOps & Infrastructure
Experience building ML pipelines and CI/CD workflows
Hands‑on experience with tools like Airflow, Prefect, Kubeflow, or similar
Experience deploying models using Docker and Kubernetes
Familiarity with cloud platforms (AWS, Azure, or GCP)
Data & Systems Integration
Experience with data processing tools (Pandas, Spark, dbt, SQL)
Ability to build and maintain data pipelines and feature stores
Experience integrating ML into production applications or APIs
Applicants must be eligible to work in Ireland without the need for future sponsorship.
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