Quantitative Model Developer, Specialist

Location: Malvern

This role:
Designs, develops, and operates production quantitative investment systems

Designs, engineers, tests, deploys, and supports software systems that directly influence investment decisions, portfolio construction, trading, risk management, and research workflows. Translates quantitative research into scalable, production-grade investment capabilities while maintaining the highest standards of reliability, reproducibility, and operational rigor.

Engineers research-to-production model delivery pipelines

Builds and maintains the architecture required to move quantitative models, datasets, and analytical processes from research through validation, testing, sandbox environments, and ultimately production deployment using modern software engineering, CI/CD, MLOps, and governance practices.

Develops advanced machine learning and AI-enabled investment capabilities

Designs and implements machine learning, statistical learning, optimization, and AI-based solutions to improve security selection, alpha research, portfolio construction, model monitoring, and investment decision support. Contributes throughout the model lifecycle including feature engineering, training, validation, deployment, observability, and ongoing performance evaluation.

Designs and implements GenAI, retrieval, and knowledge systems

Designs enterprise-grade GenAI solutions including Retrieval Augmented Generation (RAG), semantic search, knowledge retrieval, embedding architectures, reranking systems, agentic workflows, context engineering strategies, evaluation frameworks, and LLM orchestration patterns to enhance quantitative research productivity and insight generation.

Establishes software engineering and quantitative development standards

Develops reusable frameworks, libraries, services, and platform capabilities while establishing engineering standards for testing, validation, code review, performance optimization, reproducibility, data lineage, and operational excellence within quantitative investment environments.

Advances quantitative investing through emerging technology

Evaluates and applies new technologies including machine learning, deep learning, agentic AI systems, LLMs, knowledge graphs, cloud-native architectures, distributed computing, model observability tools, and software engineering best practices to improve investment outcomes and research efficiency.

Qualifications Required

Skills & Experience:

2+ years of experience in quantitative software development, financial engineering, machine learning/AI engineering, quantitative research technology, or a related quantitative technology field.

Python:
Demonstrated experience developing production-quality software using Python and modern software engineering practices.

Machine Learning / Statistical Modeling:
Experience developing, testing, and deploying machine learning, statistical learning, or optimization models.

Production Model Deployment:
Experience translating quantitative models, research, or analytical solutions into production environments.

Cloud Technologies:
Experience developing or deploying applications in cloud-based environments.

Software Engineering:
Working knowledge of system design, automated testing, version control, CI/CD, monitoring/observability, and production support.

Quantitative Analysis:
Strong quantitative, statistical, analytical, and problem-solving skills.

Education:

Bachelor's degree or higher in Computer Science, Machine Learning, Statistics, Mathematics, Engineering, Quantitative Finance, Economics, or a related quantitative discipline.

Preferred Skills & Experience Investment Management / Financial Markets:
Experience working in asset management, quantitative investing, systematic investing, factor investing, portfolio construction, trading systems, or financial markets.

Quantitative Investment Research:
Experience translating quantitative investment research into production systematic investment capabilities.

MLOps:

Experience with machine learning operations, including model deployment, monitoring, validation, and governance.

Generative AI:
Experience designing or building applications using generative AI or large language models (LLMs).Retrieval-Augmented Generation (RAG):
Experience developing RAG architectures, semantic search, knowledge retrieval, or related retrieval systems.

LLM Technologies:

Experience with vector databases, embeddings, context engineering, prompt engineering, LLM orchestration, agentic AI frameworks, or LLM evaluation.

Advanced AI/ML:

Experience…