Head of ML Ops

CarTrawler group; Dublin; On-site; EUR 80,000 - 100,000; Full time

Job summary

A leading tech company in Dublin is seeking an experienced professional to drive the MLOps strategy, align initiatives with organizational goals, and lead software development projects using cutting-edge technologies. The ideal candidate should have extensive experience in DevOps and be proficient in tools such as AWS and Docker. This role involves mentoring team members, advising senior leadership, and continuously improving data science tools and platforms. Competitive salary and collaborative environment offered.

Qualifications

• M.S. or Ph.D. in a relevant technical field, or 5+ years’ experience in a relevant role.
• Solid understanding of DevOps practices or full-stack software engineering.
• Experience leading a team or strong ability to coach high-performing DevOps engineers.
• Expertise in writing production-level Python code.
• Expertise in cloud computing services such as AWS, Google Cloud, etc.
• Expertise in containerisation technologies such as Docker, Kubernetes, etc.
• Expertise in software engineering practices: design patterns, data structures, object-oriented programming, version control, QA, logging & monitoring, etc.
• Expertise in writing unit tests and developing integration tests to ensure product quality.
• Experience and knowledge of Infrastructure as Code best practices.
• Experience in developing GenAI tools seen as a plus point.

Responsibilities

• Drive the overall MLOps strategy and align it with organizational goals.
• Lead development of innovative software tools to support Data Science solutions.
• Continuously improve architecture and evaluate emerging technologies.
• Develop tools for critical operations like release management and CI/CD pipelines.
• Champion MLOps governance by establishing lifecycle frameworks.
• Collaborate across business functions to develop a Data Science DevOps roadmap.
• Mentor and coach team members in MLOps practices.
• Serve as a point of escalation for complex technical challenges.
• Manage existing DS tools and infrastructure.

Skills

DevOps practices

Full-stack software engineering

Production-level Python code

Cloud computing services

Containerisation technologies

Software engineering practices

Infrastructure as Code

GenAI tools

Agile project management

Communication of complex tools

Education

M.S. or Ph.D. in a relevant technical field

5+ years of experience in a relevant role

Tools

AWS

Google Cloud

Docker

Kubernetes

Jenkins

Python libraries

Job description

Role Purpose

Drive the overall MLOps strategy with the Data Science & Insights (DS&I) team, collaborate with senior leadership to align strategies with broader organizational goals and objectives, lead the development of innovative software tools to serve Data Science solutions and wider business operations using cutting‑edge technologies, continuously improve architecture, establish end‑to‑end model governance, collaborate across business functions to develop a strategic DevOps roadmap, mentor and coach team members, act as a strategic advisor to senior leadership, and champion a culture of continuous learning and innovation.

Key Duties & Responsibilities

• Drive the overall MLOps strategy and align it with broader organizational goals.
• Lead the development of innovative software tools to support Data Science solutions and wider business operations using technologies such as AWS, Git, Docker, Kubernetes, and Jenkins.
• Continuously improve architecture and evaluate emerging technologies to maintain a competitive edge.
• Develop tools and services that support critical operations such as release management, source code management, CI/CD pipelines, automation, and serving ML models to production environments.
• Champion ML model‑governance by establishing a full end‑to‑end lifecycle governance framework to monitor, refresh, and perform models at optimal levels over time.
• Collaborate closely with key stakeholders across Product & Technology, IT, and Developer Experience teams to develop and prioritize a strategic Data Science DevOps roadmap that aligns with organizational objectives and drives innovation.
• Mentor and coach team members, provide guidance and expertise on advanced MLOps practices, and serve as a point of escalation for complex technical challenges.
• Act as a strategic advisor to senior leadership, providing insights, recommendations, and strategic direction on Data Science MLOps initiatives and champion a culture of continuous learning and innovation.
• Manage and maintain existing DS tools, platforms, and infrastructure, including MVT, ACDC, Action Factory, Echo, and several in‑house built Python libraries.

Reporting to: Director of Data Science & Insights

Knowledge and Key Skills

• M.S. or Ph.D. in a relevant technical field, or 5+ years’ experience in a relevant role.
• Solid understanding of DevOps practices or full‑stack software engineering.
• Experience leading a team or strong ability to coach high‑performing DevOps engineers.
• Expertise in writing production‑level Python code.
• Expertise in cloud computing services such as AWS, Google Cloud, etc.
• Expertise in containerisation technologies such as Docker, Kubernetes, etc.
• Expertise in software engineering practices: design patterns, data structures, object‑oriented programming, version control, QA, logging & monitoring, etc.
• Expertise in writing unit tests and developing integration tests to ensure product quality.
• Experience and knowledge of Infrastructure as Code best practices.
• Experience in developing GenAI tools seen as a plus point.
• Knowledge of leading cross‑function projects and R&D projects.
• Knowledge of agile project management.
• Ability to communicate complex tools and technologies clearly, precisely, and actionable to functional leaders.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.