Machine Learning Ops Engineer

Machine Learning Ops Engineer Habitat Energy is a fast growing technology company focussed on the trading and algorithmic optimisation of energy storage and renewable assets around the world. Our mission is to deliver outstanding returns to our clients to increase the attractiveness of renewable energy globally and support the transition to a clean energy future. Our rapidly growing team of over 90 professionals, spread across three key global offices - 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 a Machine Learning Ops Engineer to join our US team based in Austin, TX. This role will be embedded with the Data Science team to develop their model workflows, provide support and improvements, and manage the flow of data through our system. You will be responsible for: Develop Data Science Code: Implement robust, scalable, and efficient Python code that transforms optimization or machine learning prototypes into production-grade solutions, and maintain/improve models. Writing Well-Structured Code: Develop clean, maintainable, well-documented code that adheres to best practices. Mentor and support in the continued improvement of coding practices within the Data Science team. Supporting Data Engineering Infrastructure: Contribute to the design, development, implementation, and continuous improvement of our data engineering tools, workflows, processes, and platforms. This includes enhancing the architectural foundations and integrating new data management technologies. Model Optimization and Backtesting: Assist in development and maintenance of backtesting and model tuning frameworks. Data Quality Management: Continuously enhance data quality across multiple dimensions such as accuracy, availability, performance, and accessibility to ensure a clear understanding of data within the company. 'Must have' skills and experience: 3 years of Python experience 3 years of working with data scientist/ML researchers to develop tooling, collaborate on backtesting frameworks, build data pipelines, build/maintain orchestration workflows or productionise code. Proficiency with Orchestration and IaC (Airflow, ECS, Kubernetes, Terraform, CloudFormation), Git, Containerization (docker), SQL (Postgres, Snowflake) You are fluent in Python and its wider numerical ecosystem (Pandas, NumPy, Scikit-learn, Polars, etc.). 'Nice to have' skills and experience: Data engineering experience collecting, curating, managing and monitoring large time series data Experience with monitoring frameworks (Prometheus) Machine learning experience especially time-series forecasting & generative ML problems Optimization experience especially linear programming / mixed-integer programming. Knowledge of a US ISO power market (especially ERCOT) Knowledge of time-series forecasting & generative ML problems Understanding of probability and statistics Understanding and execution of optimization techniques (e.g. linear programming / mixed-integer programming) Proficient with optimization modelling packages and solvers Experience with data visualization and dashboard technologies (e.g. plot.ly, Dash, Streamlit) 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 offices in Austin, Texas. 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 in our candidate privacy policy here.