ML Ops Engineer

Role Overview

Machine Learning Operations (ML Ops) Engineer for Habitat Energy’s US team in Austin, Texas. Own the analytical foundation that powers trading and analytics operations, with a focus on the integrity, reliability, and long-term institutionalization of critical models.

Responsibilities

• Own MLOps for trading algorithms: operationalize algorithms into reliable distributed workflows covering feature extraction, training, evaluation, inference, and model lifecycle management.
• Integrate applied research into repeatable engineering: bring structure, repeatability, and ML engineering best practices to an evolving research environment.
• Develop forecasting & optimization capabilities for trading/analytics use cases (including financial engineering and analytical workflows).
• Build ML infrastructure & tooling to help the data science team scale model development and deployment.
• Improve execution systems across power, forecasting, and portfolio management stacks for automated trading.
• Define architectural standards & toolchains aligned with long-term strategy.
• Engineer distributed ML systems for distributed training and large-scale data processing.

Requirements

• 3+ years in MLOps, ML Engineering, or Data Engineering, building and running ML/data pipelines.
• Strong Python data/ML stack experience with tools such as Polars/Pandas, PyArrow, PySpark, NumPy/SciPy.
• Experience integrating models built with PyTorch, TensorFlow, or Keras into scalable pipelines.
• Hands-on MLOps/orchestration experience with MLflow, Ray, Prefect, or Airflow.
• Practical CI/CD for ML/data services using Git-based workflows.
• Experience with AWS (or similar cloud) and running containerized ML/data workloads in Kubernetes.

Nice to Have

• Exposure to US Power or financial markets, especially automated trading or forecasting.
• Demonstrated experience with time-series data (including financial-market-derived signals).
• Building batch/streaming pipelines (Kafka, Debezium, Spark, Flink) for CDC and real-time ingestion.
• Modern data stack tools: Iceberg, Spark/Trino/Snowflake, and advanced SQL.
• Production experience managing distributed data systems / Kubernetes clusters.
• Optimization experience (linear programming, mixed-integer programming).
• Understanding of time-series forecasting and integration of GenAI/LLMs into quantitative workflows.

Work Style / Offer

• Competitive salary and flexible working arrangements (additional details truncated in the posting).