Engineer, MLOps (Remote in Greece)

<p><span><span>ABOUT POWER FACTORS</span></span></p> <p><span> </span></p> <p><span>Power Factors is a software and solutions provider leading the next generation of clean energy with Unity, one of the most extensive and widely deployed renewable energy management suites (REMS) in the market. With over 300 GW of wind, solar, and energy storage assets managed worldwide across more than 600 customers and 18,000 sites, Power Factors manages 25% of the world’s renewable energy data.</span></p> <p><span> </span></p> <p><span>Power Factors’ Unity REMS supports the entire energy value chain, from monitoring and controls to market analytics. The company’s suite of open, data-driven applications empowers renewable energy stakeholders to collaborate, automate critical workflows, and make more informed decisions to maximize asset returns. Energy stakeholders receive end-to-end support, including solutions for SCADA &amp; PPC, centralized monitoring, performance management, commercial asset management, and field service management.</span></p> <p><span> </span></p> <p><span>With deep domain expertise, AI-powered insights are delivered at scale so businesses can optimize assets, unlock growth, and make smarter decisions as the world rapidly transitions to clean energy. Power Factors fights climate change with code.</span></p> <p><span> </span></p> <p><span>*Outside China and India </span><br/><br/></p> <p><span>ABOUT THE ROLE</span></p> <p><span> </span></p> <p><span>We are seeking a machine learning ops and devops engineer to support the growth of our AI insights products. This role will include developing processes for deploying Kubernetes and AWS-based machine learning and LLM-based products, as well as supporting team members and taking responsibility for the stability of our existing portfolio. </span></p> <p><span> </span></p> <p><span>WHAT YOU WILL NEED TO BE SUCCESSFUL</span></p> <p><span> </span></p> <p><span>The successful applicant will above all have the ability to learn fast, and be an agile and creative software engineer and problem solver.  </span></p> <p><span> </span></p> <p><span>Applicants should have: </span></p> <ul> <li><span>A bachelors’ degree in a technical subject. </span></li> </ul> <ul> <li><span>5+ years in a software engineering or ML-based role </span></li> </ul> <ul> <li><span>Familiarity with containerisation technologies such as Docker and container orchestration tools like Kubernetes. </span></li> </ul> <ul> <li><span>Significant experience with Python </span></li> </ul> <ul> <li><span>Strong desire to try new things and willing to learn with an open mind </span></li> </ul> <ul> <li><span>Motivated by working in fast-moving environments </span></li> </ul> <ul> <li><span>Fluency in English, both written and verbal </span></li> </ul> <p><span> </span></p> <p><span>Preferred qualifications: </span></p> <ul> <li><span>Experience with cloud computing platforms such as AWS, Azure, or Google Cloud Platform, including services like EC2, S3, GCP, etc. </span></li> </ul> <ul> <li><span>Hands-on experience with ML pipeline orchestration tools such as Kubeflow, MLflow, or Apache Airflow. </span></li> </ul> <ul> <li><span>Experience with Databricks </span></li> </ul> <p><span> </span></p> <p><span> </span></p> <p><span>WHAT YOU WILL BE DOING</span></p> <p><span> </span></p> <ul> <li><span>Collaborate with Data Scientists: Work closely with data scientists to understand their requirements, assist in model development, and operationalize machine learning models effectively. </span></li> </ul> <ul> <li><span>Ensure Scalability and Reliability: Optimize ML and analysis infrastructure for scalability, reliability, and performance, considering factors such as cost efficiency and resource utilization. </span></li> </ul> <ul> <li><span>Provide Technical Leadership: Provide technical leadership and guidance to cross-functional teams on best practices for MLOps, software engineering, and cloud infrastructure. </span></li> </ul> <ul> <li><span>Design and Develop ML Infrastructure: Design, implement, and maintain scalable infrastructure for machine learning workflows, including data ingestion, model training, evaluation, and deployment. </span></li> </ul> <ul> <li><span>Automate ML Pipelines: Develop automated pipelines for data preprocessing, model training, hyperparameter tuning, and model evaluation using tools such as Kubeflow, MLflow, or Airflow. </span></li> </ul> <ul> <li><span>Deploy and Monitor ML Models: Deploy machine learning models into production environments and develop monitoring solutions to track model performance, data drift, and model drift. </span></li> </ul> <ul> <li><span>Implement CI/CD for ML: Establish continuous integration and continuous deployment (CI/CD) pipelines for machine learning models, enabling rapid iteration and deployment. </span></li> </ul> <ul> <li><span>Stay Updated on ML Technologies: Stay abreast of the latest developments in machine learning, cloud computing, and De