Machine Learning Engineer

Candour Solutions

Lisboa

Presencial

EUR 50 000 - 70 000

Tempo integral

14 dias+

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Resumo da oferta

A leading insurance company in Lisbon is seeking a Machine Learning Engineer to build and maintain the infrastructure for deploying ML models. You will collaborate with data scientists and engineers to ensure seamless integration of scalable, production-grade solutions. Ideal candidates will have a strong background in Python and machine learning engineering, with experience in cloud platforms and Agile methodologies. Join us to contribute to innovative AI solutions in the insurance sector.

Qualificações

Hands-on experience in machine learning engineering, deploying and maintaining ML models.
Solid experience as a Python developer in a machine learning context.
Familiarity with cloud platforms (GCP, AWS, or Azure) and Agile methodologies.

Responsabilidades

Develop and maintain infrastructure for deploying ML models.
Build and maintain Python APIs to serve ML models.
Design and implement CI/CD pipelines for ML model deployment.

Conhecimentos

Machine learning engineering

Python programming

API development

Cloud platforms

Software engineering best practices

Agile methodologies

Formação académica

Bachelor’s/Master's degree in a quantitative field

Ferramentas

Flask

FastAPI

Terraform

Docker

CI/CD tools

Git

Descrição da oferta de emprego

Job Description – Machine Learning Engineer – Lisbon

About Hiscox:

At Hiscox we care about our people. We hire the best people for the work, and we’re committed to diversity and creating a truly inclusive culture, which we believe drives success. We embrace hybrid‑working practices, balancing the ability to work remotely with the culture and energy we experience when we are face‑to‑face in our offices. Our focus on collaboration and cross‑functional working is supported with virtual tools that minimise physical travel, hot‑desking neighbourhoods that create a physical sense of community and Team Charters that our teams co‑create to set out how they’ll work together. This modern way of working has contributed to impressive employee engagement scores across Hiscox and means we’re delivering even better solutions for our Hiscox Colleagues. As an international specialist insurer we are far removed from the world of mass market insurance products. Instead we are selective and focus on our key areas of expertise and strength – all of which is underpinned by a culture that encourages us to challenge convention and always look for a better way of doing things. We insure the unique and the interesting. And we search for the same when it comes to talented people. Hiscox is full of smart, reliable human beings that look out for customers and each other. We believe in doing the right thing, making good and rebuilding when things go wrong. Everyone is encouraged to think creatively, challenge the status quo and look for solutions. Scratch beneath the surface and you will find a business that is solid, but slightly contrary. We like to do things differently and constantly seek to evolve. We might have been around for a long time (our roots go back to 1901), but we are young in many ways, ambitious and going places. Some people might say insurance is dull, but life at Hiscox is anything but. If that sounds good to you, get in touch. You can follow Hiscox on LinkedIn, Glassdoor and Instagram (@HiscoxInsurance).

Machine Learning Engineer As a Machine Learning Engineer at Hiscox, you will play a key role in building and maintaining the infrastructure that supports the deployment of machine learning models across the London Market business unit. You’ll work closely with data scientists, platform engineers, and developers to ensure seamless integration and scalable, production‑grade machine learning solutions. You’ll be joining an award‑winning team, recognised for its pioneering collaboration with Google to deliver the market’s first AI‑enhanced lead underwriting solution, a milestone that reflects our commitment to innovation, impact, and excellence in applying machine learning to real‑world insurance challenges. This is a hands‑on engineering role focused on developing APIs, infrastructure, and deployment pipelines for machine learning models. You’ll be expected to write clean, reusable code, follow best practices in cloud and software engineering, and contribute to the operational excellence of our machine learning systems.

In addition to strong engineering skills, you’ll bring a solid understanding of data science principles. You should be comfortable reading, questioning, and interpreting machine learning models to ensure they are deployed appropriately and effectively. Your ability to bridge the gap between model development and production deployment will be key to delivering robust, high‑impact machine learning solutions. You’ll be expected to understand and implement methodologies from the ML Ops lifecycle. You’ll also be expected to work in an Agile environment, contributing to iterative development cycles, collaborating across disciplines, and adapting quickly to changing requirements.

Key Responsibilities:

Develop and maintain infrastructure for deploying ML models in both real‑time and batch environments.
Build and maintain Python APIs (Flask/FastAPI) to serve ML models.
Collaborate with cross discipline engineers to integrate ML services into user‑facing applications.
Work with platform engineers to align with infrastructure best practices and ensure scalable deployments.
Review pull requests and contribute to code quality across the MLE team.
Monitor and maintain cloud‑based ML services, ensuring reliability and performance.
Design and implement CI/CD pipelines for ML model deployment.
Write unit tests and follow object‑oriented programming principles to ensure maintainable code.
Support data modelling and cloud networking tasks as needed.
Contribute to the development and improvement of our model registry, including tracking and implementation of model discontinuation upgrades and model monitoring.

Person Specification To succeed in this role, you’ll typically have:

Bachelor’s/Master’s degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics, Engineering) or equivalent.
Hands on experience in machine learning engineering, including deploying, monitoring, and maintaining ML models in production environments.
Experience in finance or insurance is an advantage but not required.
Solid experience as a Python developer, ideally in a machine learning engineering context.
Strong understanding of software engineering best practice.
Experience with TDD.
Experience with infrastructure as code tools like Terraform.
Hands on experience with cloud platforms (GCP, AWS, or Azure).
Familiarity with containerization using Docker and orchestration of deployments.
Experience with CI/CD tools and Git‑based development workflows.
Understanding of API operations monitoring and logging.
Strong problem‑solving skills and ability to work independently on technical tasks.
Familiarity with Agile methodologies and experience working in Agile teams.