Arbetsbeskrivning
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.
What You’ll Do:
- 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
Who You Are:
- Experience as an MLOps Engineer or in a similar role focused on machine learning infrastructure and operations
- Hands-on experience with MLOps tools, such as MLflow for model tracking, versioning, and monitoring, and Databricks for collaborative development and deployment
- Proficiency in cloud platforms, preferably AWS (e.g., Lambda, SageMaker, EC2), and familiarity with data engineering tools and practices
- Familiarity with DevOps practices, including Docker, CI/CD pipelines, Git, and automated testing
- Understanding of scalable model deployment, data pipelines, and the end-to-end model lifecycle
- A collaborative mindset with experience working closely with data scientists, data engineers, and cross-functional teams
LocationOur beautiful office is located in the heart of Södermalm just a short walk from Slussen subway station. We encourage in-office collaboration but support a hybrid work model.
ApplyTo apply, please submit your resume and a cover letter highlighting your relevant experience and what you think you could bring to our team. Throughout the recruitment process you will meet with the Talent Acquisition Specialist, Head of Implementation & Operations,VP Trading, and our CEO John.
Our corporate language is English, as we have over 20 nationalities in the office. We therefore appreciate it if you could submit your CV in English.
We look forward to hearing from you!
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We kindly but firmly decline any engagement in recruitment assistance for our hiring processes. This includes partnership offers or the sale of recruitment tools.