Machine Learning Operations Engineer

at  Flower Infrastructure Technologies AB

What We DoSwiftly gaining ground as Sweden’s industry leader in battery storage and expanding rapidly in selected European markets, Flower is on a mission to enable the energy system of tomorrow.
With an industry-leading AI-powered platform at its core, our service includes stabilizing the energy system by enhancing predictability and flexibility for both energy producers and consumers. By combining pioneering technology with a portfolio of flexible energy assets, we break new ground towards a fossil-free energy system, allowing clean energy to power society.
Who We Are Tech company at heart – purpose in our DNA. Flower consists of a diverse group of innovative individuals with a strong desire to improve the state of the world.
At Flower, we believe trust, collaboration and diversity are essential to not only create an inclusive work environment, but also drive career growth. By embracing varying perspectives, we allow creativity and progress to flourish.
To accelerate towards our goal of becoming the pioneering force powering the energy system of tomorrow, we are now looking for a passionate and skilled Machine Learning Operations Engineer.
About The Role: We are looking for a proactive and skilled MLOps Engineer to join our Trading domain. In this role, you will build and maintain robust infrastructure and processes to support the entire lifecycle of our machine learning models, from development to deployment and monitoring in production. Collaborating closely with our Data Engineers and Data Scientists, you will ensure the seamless integration of models into our trading systems. Your expertise will enable efficient model deployment, versioning, and monitoring, ensuring our data-driven solutions operate reliably and effectively at scale.

Work closely with data engineers and data scientists in order to set up proper workflows around data management to support data exploration, model training and model development.
Collaborate with data scientists to implement model versioning, experiment tracking, and monitoring frameworks.
Implement and maintain model quality checks, performance monitoring, and alerting systems.
Create and maintain CI/CD pipelines for model deployment in GitHub and cloud environments (AWS)
Build and optimize model training and inference systems.
Create and maintain comprehensive documentation of MLOps pipelines, processes, and procedures