ML Ops Engineer

ML 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 100+ 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 a Machine Learning Operations Engineer to join our US team based in Austin. This role is for a Software Developer embedded within the Data Science team. The ideal candidate will have excellent Python skills, write clean and robust code, and possess strong communication abilities to foster close collaboration between the Data Science and Software Engineering teams. A natural inclination toward documentation and quality assurance is essential.

You will be responsible for:

Software Development Lifecycle (SDLC)

• Implement robust, scalable, and efficient Python code that transforms machine learning and optimization prototypes into production-grade solutions, in line with our SDLC process.
• Assist in developing and maintaining frameworks for model backtesting and optimization to ensure models perform well in live environments.

Applications

• Provision and development of tooling and infrastructure to enable the running of ML models produced by the data science team, including workflows, platforms and tooling.
• Contribute to the development and continuous improvement of data engineering tools, workflows, and platforms to ensure smooth deployment and operation of ML models.

Asset and Market Comms and Data Management

• Enhance systems that manage the flow of forecast and optimization data to ensure seamless integration with operational models and analytics.

Forecasting & Optimization Capability Development

• Transform forecasting and optimization models into production-ready solutions, ensuring they are optimized for live operations, and contribute to continuous optimization and backtesting of models.

Tool Selection and Architectural Standards

• Contribute to selecting tools and defining architectural standards that align with the company's goals and technology strategy.
• Participate in the selection of tools that support scalable data infrastructure for ML workflows and optimization models.

CI/CD Pipeline

• Support the integration of ML models into a CI/CD pipeline, automating deployment and ensuring stable operations of models in production.

Preferred 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.
• BA/BSc degree in Computer Science, Machine Learning, Electrical Engineering, or related technical field.
• Proficiency with Orchestration and IaC (Airflow, Prefect, Ray, ECS, Kubernetes, Terraform, CloudFormation), Modern data strategies (Iceberg, Trino), 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 timeseries data
• Experience building and maintaining distributed systems for data and ML applications using Dask, Trino, Spark, Ray, or similar frameworks.
• Experience tuning GPU-based applications for performance and cost efficiency using frameworks like CUDA, PyTorch, TensorFlow, or TensorRT, with a deep understanding of their tradeoffs across training, inference, and data processing workload
• Experience with monitoring frameworks (Prometheus)
• Machine learning experience especially time-series forecasting & generative ML problems
• Optimization experience especially linear programming / mixed-integer programming.
• Data engineering experience collecting, curating, managing and monitoring large timeseries data
• Experience with monitoring frameworks (Prometheus)
• Machine learning experience especially time-series forecasting & generative ML problems
• Optimization experience especially linear programming / mixed-integer programming.

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 offices in Austin.

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