ML Ops Engineer

Role Overview

ML Ops / MLOps Software Engineer for Habitat Energy’s US team (Austin, Texas). You will own the analytical foundation that powers trading and analytics operations, with a focus on the integrity, reliability, and long-term institutionalisation of critical models—especially forecasting, optimisation, financial engineering, and analytical workflows.

Responsibilities

• Trading Model Deployment: Productionise convex optimisation models and market forecasts; translate market hypotheses from researchers and traders into robust, live systems.
• Forward-Deployed Engineering: Bridge research and core software engineering; rapidly prototype on the desk while implementing scalable practices (e.g., version control, testing, performance profiling).
• Research & Data Infrastructure: Build and improve data engineering tools, backtesting frameworks, and research environments; ensure high-fidelity data ingestion and shared data understanding.
• Cross-Functional Execution: Collaborate with Trading, Quantitative/Applied Analytics, and Core Tech to enhance modelling capabilities for front-office objectives.
• Live Desk Support: Provide rapid-response troubleshooting, tooling creation, and escalation support for live trading applications and models (includes out-of-hours escalation).
• Mentorship & Leadership: Mentor more junior teammates and provide guidance on technical skills and working practices.
• Security & Architecture: Design with security, efficiency, scalability, and operational impact in mind; maintain proactive defence against external threats.

Requirements

• 3+ years of Python experience.
• 3+ years in DevOps, MLOps, Data Infrastructure, or closely related roles, delivering data-intensive, quantitative applications in high-reliability environments.
• Experience deploying and operating services on Kubernetes.
• Strong experience building and maintaining CI/CD pipelines.
• Strong command of the modern data stack, including:
  • Iceberg (open table formats)
  • Compute engines: Spark, Trino, Snowflake
  • Advanced SQL
• Hands-on experience with MLOps/orchestration tools such as MLflow, Ray, Prefect, or Airflow.

Nice to Have

• Exposure to US power or financial markets, especially automated trading or forecasting.
• Experience building production trading systems or high-availability software.
• Experience with batch and streaming pipelines (e.g., Kafka, Debezium, Spark, Flink) for CDC and real-time ingestion.
• Experience managing distributed data systems or Kubernetes clusters in production.
• Optimisation experience.