ML Ops Engineer

Machine Learning Operations Engineer

Habitat Energy is a fast growing technology company focused on the physical and financial optimisation of energy storage and renewable generation assets globally through complex models and trading. By maximising the returns from these assets we aim to drive investment in renewable energy and accelerate the transition to a low carbon world. Our rapidly growing team of 130+ people in Austin, TX, Oxford, UK, and Melbourne, Australia brings together exceptionally talented and passionate people in the domains of energy trading, data science, software engineering and renewable energy management.

We have a vacancy for an ML Ops Engineer to sit directly alongside our trading and research teams. In this role, you will be the critical link between our trading and research teams and our core software engineering group. Your core focus will be rapid iteration, building robust market data pipelines, and bringing advanced analytical, convex optimization, and fundamental forecasting models to life in production to directly drive commercial outcomes.

Your responsibilities will include:

• Trading Model Deployment: Take ownership of productionising complex convex optimization models and fundamental market forecasts. You will partner closely with researchers and traders to translate market hypotheses into robust, live systems
• Forward-Deployed Engineering: Act as the technical bridge between research and core software engineering. You will rapidly prototype solutions on the desk while simultaneously advocating for and implementing scalable engineering practices (version control, testing, performance profiling) within the trading and research teams
• Research & Data Infrastructure: Build and continuously improve our data engineering tools, backtesting frameworks, and research environments. Champion data quality by ensuring high-fidelity ingress for critical market and fundamental datasets, creating a reliable and shared understanding of data across the trading and technical teams
• Cross-Functional Execution: Collaborate tightly across Trading, Data Science, and Core Tech to build consensus and ensure our core architecture supports advanced quantitative strategies and rapid iteration
• Live Desk Support: Provide rapid-response troubleshooting, tooling creation, and escalation support for live trading applications and models. Please note this role includes an out-of-hours escalation component
• Mentorship & Leadership: Mentor more junior team members and provide regular guidance on technical skills, working practices, and career development
• Security & Architecture: Think holistically about security, efficiency, scalability, and operational impact when designing solutions, while maintaining proactive defense against external threats
• AI-Assisted Workflow Management: Set team standards and best practices for AI-assisted workflows, ensuring tools are used to raise quality and critically reviewing AI-generated output for architectural decisions and security vulnerabilities
  
  

Requirements

Preferred skills and experience:

• 5+ years of Python experience
• Fluent in Python's quantitative and numerical ecosystem (e.g. Pandas, NumPy, Polars, Pydantic)
• 3+ years of experience building robust data pipelines, delivering production code, and developing or improving backtesting frameworks, ideally within a fast-paced commercial or trading environment
• Hands-on experience orchestrating complex data and analytics workflows
• Proficiency with cloud infrastructure, containerisation, and orchestration tools (e.g., Docker, Kubernetes, Terraform, Airflow/Prefect, RabbitMQ), as well as relational database management (Postgres, Alembic)
• Demonstrated ability to influence research/data science teams and bridge the gap between experimental code and production-grade software
• Experience reviewing work produced by peers and providing constructive, specific feedback
• A strong understanding of security best practices and the ability to apply them routinely
• Ability to independently translate requirements into working solutions and effectively document design decisions
• Experience using AI coding tools productively, alongside a strong understanding of their security implications and data leakage risks
  
  

‘Nice to have' skills and experience:

• Domain knowledge of UK power markets
• Hands-on experience with convex optimization libraries/solvers (e.g., CVXPY) and building fundamental or statistical forecasting models
• Familiarity with time-series forecasting, quantitative modeling, or machine learning techniques (e.g., feature engineering, LightGBM)
• Experience centralising high-volume datasets for analytics and ML, including archiving to Parquet on S3
• Experience with monitoring frameworks (e.g., Prometheus) and building desk-facing visualizations/dashboards (e.g., Grafana, Superset)
  
  

Ultimately we are looking for someone who is a great fit for our company so we encourage you to apply even if you may not meet every requirement in this posting. We value diversity and our environment is supportive, challenging and focused on the consistent delivery of high quality, meaningful work.

In return, we'll give you a competitive salary, flexible working arrangements and a lot of personal development opportunities. We operate a hybrid working model with at least 2 days in our office in Oxford.

When you apply for a job with us, we process some of your personal information. You can find out more about how we process your information on our company website: https://habitat.energy/privacy-policy/.