ML Ops Engineer (Hybrid) — Scale Trading Models

Habitat Energy; Austin (TX); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Hybrid work model

Personal development opportunities

Job summary

Habitat Energy in Austin, Texas, seeks a Machine Learning Operations Engineer to own the analytical foundation powering trading and analytics. You will operationalize models, ensure reliability, and contribute to a scalable ML infrastructure for analytics workflows.

The role emphasizes integration of models into robust pipelines, collaboration with Quantitative Analytics, and working in AWS/Kubernetes environments to support forecasting, optimization, and data-driven decision making.

Qualifications

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

Responsibilities

• MLOps Ownership: Operationalize trading algorithms into reliable, distributed workflows across feature extraction, training, evaluation, inference, and model lifecycle management.
• ML Infrastructure: Build tooling and platforms to scale model development and deployment.

Skills

MLOps

Python stack

Distributed ML

Cloud AWS

Tools

MLFlow

Ray

Prefect

Airflow

Job description

Habitat Energy in Austin, Texas, seeks a Machine Learning Operations Engineer to own the analytical foundation powering trading and analytics. You will operationalize models, ensure reliability, and contribute to a scalable ML infrastructure for analytics workflows.

The role emphasizes integration of models into robust pipelines, collaboration with Quantitative Analytics, and working in AWS/Kubernetes environments to support forecasting, optimization, and data-driven decision making.