Machine Learning Engineer

Job Overview

Processing thousands of energy data points per second from diverse operational sources, handling massive volumes of energy data while running sophisticated classification and anomaly detection models in real-time, maintaining comprehensive data lineage, and delivering insights through high-performance platforms used by energy operators globally requires exceptional engineering and scientific expertise. This processing demands models that can withstand the scrutiny of energy analysts and traders, operations teams, and regulatory bodies, with the performance, stability, and reliability that critical energy systems require.

Key Responsibilities

• Design and build ML infrastructure and applications to propel the design, deployment, and monitoring of existing and new ML pipelines and models.
• Ensure 100% uptime and bulletproof fault-tolerance of every component of our ML platform.
• Bridge the gap between research experiments and production energy systems.
• Work with software engineers, data scientists, and energy analysts and traders.

Required Qualifications

• Experienced in building and deploying distributed scalable ML pipelines using Kubernetes and MLflow.
• Solid machine learning engineering fundamentals, fluent in Python, PyTorch, and XGBoost.
• Skilled in developing classification models and anomaly detection systems for production environments.
• Capable of implementing comprehensive data lineage tracking and model governance systems.
• Driven by working in an intellectually engaging environment with top energy analysts and traders and technology experts.
• Excited about working in a dynamic environment: not afraid of complex energy challenges, eager to bring new ML innovations to production, and a positive can-do attitude.
• Passionate about mentoring team members, helping them improve their ML engineering skills and grow their careers.
• Experienced with the full ML model lifecycle, including experiment design, model development, validation, deployment, monitoring, and maintenance.

Preferred Qualifications

• Experience in the energy sector or understanding of energy systems and operations.
• Practical experience with AWS services (SageMaker, S3, EC2, Lambda, etc.).
• Experience with infrastructure as code tools (Terraform, CloudFormation).
• Experience with Apache Kafka and real-time streaming frameworks.
• Familiar with observability principles such as logging, monitoring, and distributed tracing for ML systems.
• Experience with transformer architectures and generative AI applications in operational contexts.
• Experience with time series analysis and forecasting techniques relevant to energy applications.
• Knowledgeable about data privacy regulations and compliance frameworks in the energy sector.

Benefits

Enjoy flexible hybrid working – split your time between home and our office, with the freedom to work where you’re most productive.

A vibrant, diverse company pushing ourselves and the technology to deliver beyond the cutting edge.

A team of motivated characters and top minds striving to be the best at what we do at all times.

Constantly learning and exploring new tools and technologies.

Acting as company owners (all Vortexa staff have equity options)– in a business-savvy and responsible way.

Motivated by being collaborative, working and achieving together.

Private Health Insurance offered via Vitality to help you look after your physical health.

Global Volunteering Policy to help you ‘do good’ and feel better.