ML Engineer- Senior or Mid level

Vortexa; City Of London; Hybrid; GBP 80,000 - 100,000; Full time

Job summary

A leading energy data firm in London is seeking an experienced professional to design and build machine learning infrastructure for energy applications. The role involves bridging the gap between research and production systems, ensuring reliability, and supporting the monitoring of ML pipelines. Candidates should possess strong knowledge in ML engineering and be excited to tackle complex energy challenges. The company offers hybrid working and opportunities for growth.

Benefits

Private Health Insurance

Global Volunteering Policy

Equity options for all staff

Qualifications

• Experience in building and deploying ML infrastructure.
• Solid fundamentals in machine learning engineering.
• Comfortable in a fast-paced, dynamic environment.
• Experience with the full ML model lifecycle.

Responsibilities

• Design and build ML infrastructure for energy applications.
• Bridge research and production systems for ML models.
• Support the monitoring of ML pipelines to ensure reliability.

Skills

Building and deploying distributed scalable ML pipelines

Fluency in Python

Experience with PyTorch

Experience with XGBoost

Development of classification models

Knowledge of anomaly detection systems

Model governance systems

Mentoring team members

Tools

Kubernetes

MLflow

AWS services

Apache Kafka

Terraform

CloudFormation

Job description

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.

The Data Platform Team is responsible for all machine learning operations across our energy data ecosystem. We work with everything from raw sensor data from millions of energy assets to complex operational datasets, generating high-value predictions such as equipment failure detection, energy demand forecasting, operational anomaly identification, predictive maintenance scheduling, and system optimization recommendations.

The team has built a comprehensive suite of statistical and machine learning models that enable us to provide the most accurate and actionable insights into energy operations. We take pride in applying cutting-edge research to real-world energy challenges in a robust, scalable, and maintainable way. The quality of our models is continuously validated by experienced in-house energy analysts and traders and domain experts to ensure reliability of our predictions.

You'll be instrumental in designing and building ML infrastructure and applications to propel the design, deployment, and monitoring of existing and new ML pipelines and models. Working with software engineers, data scientists, and energy analysts and traders, you'll help bridge the gap between research experiments and production energy systems by ensuring 100% uptime and bulletproof fault-tolerance of every component of our ML platform.

You Are:

• Experienced in building and deploying distributed scalable ML pipelines that can process large volumes of energy data daily using Kubernetes and MLflow
• With 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, where constructive challenges and technical debates are encouraged
• 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

Awesome If You:

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

• 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