Data Structures Jobs in Kingston Upon Thames

2 of 2 Data Structures Jobs in Kingston Upon Thames

Treasury Quantitative Developer - Data Engineer (London)

Surbiton, Greater London, UK
Millennium Management
Treasury Quantitative Developer - Data Engineer Responsibilities Take part in the development and enhancement of the back-end distributed system, providing high performance and high availability margin and stress cash calculations and simulations to Senior Management, Portfolio Managers and Treasurers. Work closely with Quant researchers and developers, tech teams, middle office and business management teams in London, New York, Tel … Aviv & Miami. Design, develop and maintain data models, pipelines and warehouse and caching stores Requirements Must-have qualifications/skills: Minimum 5+ years of experience developing systems in Python or other OOP background with Python knowledge. B.A. in computer science or another quantitative field. Experience with Cloud technologies Experience working with RDBMS (Postgres preferred) and other database technologies (data lakes, DuckDB, NoSQL) Good understanding of Design Patterns, Algorithms & Data structures Experience working with Git/GitHub and with CI/CD pipelines Ability to communicate effectively with senior stakeholders across the organization Able to work independently in a fast-paced environment. Detail oriented, organised, demonstrating thoroughness and strong ownership of work. Nice-to-have qualifications/ More ❯
Employment Type: Full-time
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Founding Machine Learning Engineer (London)

Surbiton, England, United Kingdom
JR United Kingdom
strong software engineering background and a passion for deploying AI/ML models into real-world, production-grade applications. Apply if: You have strong foundational software engineering knowledge, including data structures, algorithms, system design, and OOP. You have advanced knowledge of LLM architectures and ML/DL frameworks (e.g., TensorFlow, PyTorch, LangChain, Keras, scikit-learn). You're … and associated systems for performance, scalability, and cost-effectiveness in a production environment. Implement and manage the infrastructure for MLOps, including fine-tuning, deployment, monitoring, and versioning. Develop robust data pipelines for ingestion, cleaning, model training, and continuous deployment. Build retrieval-aware repositories for model training, evaluation, and real-time, context-rich inference. Collaborate closely with software engineers to More ❯
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