At a Glance
- Tasks: Build and scale ML infrastructure for advanced trading systems using cutting-edge technologies.
- Company: Established quantitative tech firm with a focus on innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative culture with excellent career advancement opportunities.
- Why this job: Join a dynamic team and work with petabytes of data in a high-performance environment.
- Qualifications: Strong Python skills and experience with MLOps and large-scale data processing.
The predicted salary is between 63000 - 77000 £ per year.
An established quantitative technology firm is seeking an experienced ML Dev Ops Engineer to build and scale the machine learning infrastructure that powers advanced research and production trading systems
Working with one of the industry's largest market data environments, you'll design the platforms and tooling that enable data scientists and quantitative researchers to efficiently develop, train, deploy, and monitor machine learning models at scale.
This is an opportunity to work on cutting‑edge infrastructure handling petabytes of time‑series data in a high-performance, low‑latency environments.
Key Responsibilities
- Design and build a scalable feature store for versioning, storing, and serving time‑series features for machine learning workloads.
- Develop and maintain end‑to‑end MLOps pipelines covering data ingestion, feature engineering, model training, backtesting, validation, and deployment.
- Create robust data ingestion frameworks that transform raw data streams into structured, analytics‑ready formats using modern data lake technologies.
- Deploy production‑grade feature computation and model inference services with appropriate latency and reliability characteristics.
- Collaborate with engineering, quantitative research, and platform teams to integrate ML infrastructure with existing data capture and execution systems.
- Improve platform observability, monitoring, reliability, and operational performance across the ML ecosystem.
- Required Experience
- Strong software engineering experience in Python, particularly for data engineering and machine learning infrastructure.
- Proven experience building and operating feature stores, MLOps platforms, or large‑scale machine learning infrastructure.
- Hands‑on experience designing and managing data lakes and processing large‑scale datasets (terabyte to petabyte scale).
- Experience deploying and maintaining production systems with monitoring, automation, and high reliability requirements.
- Familiarity with distributed computing frameworks such as Spark, Ray, Dask, or similar technologies.
- Technical Skills
- Advanced Python development, with occasional integration points into C/C++ environments.
- Experience with modern data storage and processing technologies, including Parquet, distributed data lakes, and scalable compute platforms.
- Experience using workflow orchestration tools such as Airflow, Prefect, or similar.
- Comfortable working with large‑scale, real‑time, high‑frequency time‑series data.
- Exposure to C or C++.
- Experience working with real‑time streaming or time‑series datasets.
- Knowledge of modern table formats and data lake technologies such as Apache Iceberg, Delta Lake, or similar.
- #J-18808-Ljbffr
ML DevOps Engineer employer: Talentedge
Join a dynamic e-commerce start-up that values innovation and agility, offering you the chance to take ownership of financial processes in a fast-paced environment. With a strong emphasis on employee growth and collaboration, you'll work closely with founders and senior leadership, ensuring your contributions directly impact the company's success. Enjoy a vibrant work culture that encourages creativity and provides unique opportunities for professional development.
