About the Role / About the Job
The company is seeking an AI Engineer to develop, deploy, and maintain artificial intelligence and machine learning systems supporting systematic investment strategies. The role combines applied machine learning with software engineering to turn research ideas into reliable, scalable production systems. You will work closely with quantitative researchers and investment colleagues across the model lifecycle, from data preparation and experimentation to deployment, monitoring, and continuous improvement.
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Key Responsibilities
- Develop and implement machine learning models and AI applications supporting investment research, market analysis, and systematic trading.
- Collaborate with quantitative researchers to translate research prototypes into tested, maintainable production code.
- Build and maintain data pipelines for ingestion, validation, transformation, and feature generation.
- Develop reusable tools for model training, evaluation, experiment tracking, and reproducible research.
- Implement robust validation processes, addressing data leakage, overfitting, and consistency between training and production environments.
- Build model inference services and integrate outputs with portfolio, risk, and trading systems.
- Automate model deployment and version management, with appropriate testing, release controls, and rollback procedures.
- Monitor data quality, model behavior, and system performance, including drift, latency, failures, and resource usage.
- Investigate production issues and improve system resilience through logging, alerts, recovery procedures, and clear operational documentation.
- Optimize computational workloads to balance performance, scalability, and cost.
- Evaluate new AI methods and tools against practical requirements, adopting them where they deliver measurable improvements.
- Maintain code quality through testing, peer review, version control, and technical documentation.
- Protect confidential data and intellectual property, and follow the firm’s security, risk, and compliance policies.
Qualifications and Skills
- A degree in computer science, engineering, mathematics, statistics, or a related discipline, or equivalent practical experience.
- Strong proficiency in Python and sound software engineering fundamentals.
- Practical experience developing and evaluating machine learning models using frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Understanding of statistical modeling, model evaluation, and the limitations of machine learning.
- Experience building data pipelines and working with SQL, structured datasets, and APIs.
- Familiarity with model deployment, monitoring, and the operational requirements of production systems.
- Experience with version control, automated testing, Linux, and containerized environments.
- Strong debugging and problem-solving skills, with attention to reliability and maintainability.
- Ability to communicate technical choices and limitations clearly and collaborate across research and engineering teams.
Desirable Experience
- Experience in quantitative finance, systematic trading, or financial data engineering.
- Familiarity with time-series modeling, Bayesian methods, deep learning, or optimization.
- Experience with cloud infrastructure, workflow orchestration, and continuous integration and deployment.
- Knowledge of GPU computing, distributed processing, or performance optimization.
- Experience with large language models, including fine-tuning, retrieval, and systematic evaluation.
- Familiarity with market data feeds, portfolio systems, or trading infrastructure.
- Experience implementing access controls, audit trails, and recovery procedures for critical systems.
What Success Looks Like
Success means delivering dependable AI systems that help the firm move from research to production efficiently. This includes reproducible model workflows, reliable data pipelines, well-tested deployments, effective monitoring, and measurable improvements in research productivity and operational performance.
