Machine Learning DevOps Engineer

London - UK

Monthly Salary: Not Disclosed

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

We are seeking highly motivated and talented people to join the Boerboel team. As a member of the team you will be working with passionate and curious colleagues who thrive on solving complex problems. Boerboel is an office-first firm. While we offer flexibility to our employees remote work is not available at this time.

About us:
Boerboel Trading is an electronic trading firm active across a wide range of asset classes and regions with offices in NYC Chicago London and Malta. We engage in rigorous quantitative analysis to deploy systematic trading strategies over multiple time horizons. Trading and research employees of the firm are graduates from a wide spectrum of quantitative disciplines such as engineering computer science mathematics and physics. Our employees apply their scientific methodologies to the analysis of extensive financial data sets.

Summary:
We operate high-performance infrastructure processing 20PB of market data. Were building a production ML platform to organise features orchestrate model training and serve predictions for systematic strategies. As a ML DevOps Engineer youll design and implement the feature store data lake organisation and ML pipelines that enable our research and trading teams to work efficiently at scale.

Objectives:

Feature store & data lake: Build scalable infrastructure for time-series feature storage retrieval and versioning optimized for ML workloads
MLOps pipelines: Design end-to-end workflows for data ingestion feature engineering model training backtesting and deployment
Data ingestion layer: Connect raw data streams into structured queryable formats (Parquet/Delta Lake)
Production serving: Deploy feature computation and model inference with appropriate latency characteristics
Integration: Work with existing data capture and execution systems

Experience:

Python depth: Strong Python engineering with focus on data pipelines and ML infrastructure
ML platform experience: Built feature stores MLOps pipelines or similar systems at scale (Netflix Yandex Criteo or comparable tech companies)
Data lake expertise: Experience ingesting organising and serving large datasets (TB-PB scale)
Production mindset: Deployed systems with reliability monitoring and performance requirements
Distributed computing: Familiarity with Spark Dask Ray or similar frameworks

Required skillset:

Python-first with some C integration points
Modern data stack: Parquet Data Lake distributed compute
Your choice of orchestration tools (Airflow Prefect etc.)
20PB storage nanosecond-resolution data real-time streams

Preferred:

C exposure (helpful for integration but not required)
Real-time or time-series data experience
Knowledge of Data Lake Iceberg or modern table formats

Objectives:

Feature store & data lake: Build scalable infrastructure for time-series feature storage retrieval and versioning optimized for ML workloads
MLOps pipelines: Design end-to-end workflows for data ingestion feature engineering model training backtesting and deployment
Data ingestion layer: Connect raw data streams into structured queryable formats (Parquet/Delta Lake)
Production serving: Deploy feature computation and model inference with appropriate latency characteristics
Integration: Work with existing data capture and execution systems

Experience:

Python depth: Strong Python engineering with focus on data pipelines and ML infrastructure
ML platform experience: Built feature stores MLOps pipelines or similar systems at scale (Netflix Yandex Criteo or comparable tech companies)
Data lake expertise: Experience ingesting organising and serving large datasets (TB-PB scale)
Production mindset: Deployed systems with reliability monitoring and performance requirements
Distributed computing: Familiarity with Spark Dask Ray or similar frameworks

Required skillset: