Senior Machine Learning Engineer (Contract)
At a Glance
- Senior level contract: 6 months, with extension possible
- Azure (preferred) or AWS, MCP, A2A, MLOps and LLMOps
- Hybrid, around 2 days a week on site in Dublin (NI commuters welcome)
- Extremely competitive day rate
- Move deeper into agentic infrastructure with a principal engineer as your mentor
About the Company
Our client is an established insurance business based in Dublin, investing seriously in agentic AI alongside a mature conventional machine learning estate. The programme is delivered in partnership with one of the world's largest IT services and consulting providers, bringing global AI expertise and tooling to the work. It's a rare chance to shape a modern agent platform inside a regulated enterprise, where engineering decisions carry real weight.
The Role
You'll be part of the engineering backbone of the agent estate, building and running the platform that takes agents and ML models into production. Working under the architectural direction of the Principal Machine Learning Engineer, you'll implement and help evolve the platform, with room to take on more ownership over time. Day to day you'll partner with the data science group to turn model and agent designs into reliable, deployed services. It's a strong fit for a hands on platform or MLOps engineer who has shipped production ML and wants to grow into agentic and LLM infrastructure.
Key Responsibilities
- Build and maintain deployment pipelines, monitoring and reliability tooling for agentic and conventional ML systems
- Implement CI/CD pipelines and infrastructure as code for ML and agentic services
- Develop and deploy MCP servers and integrate A2A agent communication into existing services
- Deploy conventional ML models to production and support their ongoing operation
- Contribute to greenfield builds that integrate with legacy systems and data sources
- Flag integration risk early so it can be designed around
- Support monitoring, logging, alerting and evaluation practices for models and agents in production
- Collaborate with the data science group to turn model and agent designs into deployed services
What You'll Need
Essential:
- 4+ years in machine learning or platform engineering
- Experience building AI infrastructure on Azure (preferred) or AWS
- Hands on experience deploying ML models to production, beyond training and experimentation
- Some exposure to greenfield development integrated with legacy systems
- Python, SQL, Docker or Podman, and Terraform
- Practical experience of CI/CD in an ML context
- MCP server deployment and A2A (Agent2Agent protocol), or agent to agent orchestration experience you can map onto them quickly
- Working knowledge of MLOps and LLMOps
- Eligible to work in Ireland and able to be in the Dublin office around 2 days a week
Desirable / Nice to Have:
- Azure AI Foundry exposure
- RAG systems and vector stores such as pgvector or Pinecone
- An agent framework or SDK, such as the Claude Agent SDK
Why Apply?
- Extremely competitive day rate
- 6 month contract with extension possible
- Hybrid working, around 2 days a week on site in Dublin, with NI commuters welcome
- Learn directly from a principal engineer, with a clear path toward architecture ownership
- Work hands on with MCP and A2A on a greenfield agentic platform
- See your work run in production inside a regulated enterprise
Next Steps
Interested? Send your CV to Aaron at , or connect with Aaron on LinkedIn for a confidential chat.