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Senior Machine Learning Engineer

Location
Dublin, City of Dublin, Ireland
Job Type
Permanent
Salary
£70.3k - 105.4k per year + benefits + bonus
Sector
Technology
Reference
ML112
Posted
Posted 0 hours ago

Senior Machine Learning Engineer

 

Role: Senior ML Engineer

Location: Dublin (hybrid working)

Salary: flexible depending on experience + great benefits

All applicants must be based in Ireland, with a valid work permit.

 

Opportunity to join a fantastic Dublin-based AI start-up. Where the people make the product and the founders ethos is that happy employees make for happy customers.

Want autonomy? Strategic product input? Want to see your works direct success impact? Be seen and heard, not counted as a number?

This could be the opportunity for you!

 

Delivering new-age solutions, integrated with the latest LLMs and ML tools, seen as the go to B2B SaaS solution in their respective markets, for household names in the tech market and beyond...

What we need from you:

  • Keen technologist, love keeping up to date with latest tech, tools, and trends
  • Humble, open mind to learning and collaborative working, happy to knowledge share and develop others
  • Team player, happy to join in with office socials, dinners/drinks after work, team events, company days
  • Passion and enthusiasm, you'll be working alongside great people with a fantastic team ethos, sharing vision for the products and the business goals is important.
  • Management, the ability to coach and develop other team members, as well as ensuring technical understanding for key stakeholders across the business, whilst engaging regularly with the Leadership team.

Skills and experience:

  • Delivered full lifecycle machine learning solutions, from data ingestion to model deployment, using Python and leading ML frameworks.
  • Hands-on experience building and optimising models with TensorFlow, PyTorch, and/or related libraries.
  • Developed scalable data pipelines and preprocessing workflows to support ML applications.
  • Proficient in core data engineering practices, including data wrangling, transformation, and storage optimisation.
  • Applied MLOps best practices for model versioning, reproducibility, and continuous deployment.
  • Skilled in containerisation and orchestration using Docker and Kubernetes to manage ML workloads.
  • Leveraged MLflow and/or similar tools for experiment tracking, model registry, and deployment automation.
  • Strong advocate of cross-functional collaboration in Agile and DevOps-driven engineering teams.

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