At a Glance
- Tasks: Build and deploy machine learning models for financial data and automation.
- Company: Join a forward-thinking company at the forefront of financial AI.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with exciting projects in risk and fraud detection.
- Why this job: Make a real impact in the financial sector with cutting-edge AI technology.
- Qualifications: Experience in machine learning, Python, and deploying models in production.
The predicted salary is between 63000 - 77000 £ per year.
Role Overview
This role focuses on building machine learning models used across financial data, risk, and automation use cases.
You’ll work alongside engineers and product teams to deploy ML solutions into production.
Key Responsibilities
- Develop and deploy ML models
- Work with structured financial datasets
- Collaborate with engineering teams
- Improve model performance and monitoring
- Support production ML pipelines
- Required Experience
- Machine learning engineering experience
- Python and ML frameworks
- Experience deploying models to production
- Financial services or data-heavy background
- Cloud ML tooling exposure
- Nice to Have
- NLP or time-series modelling
- MLOps experience
- Risk or fraud use cases
- Why Join
- Applied ML in production
- High-impact financial use cases
- Growing AI capability
- #J-18808-Ljbffr
Machine Learning Engineer - Financial AI employer: EC1 Partners
EC1 Partners is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration among talented professionals in the heart of London. With a strong focus on employee growth, we provide ample opportunities for skill development and career advancement, all while working on cutting-edge high-frequency trading technologies that make a significant impact in the financial markets.
We think you need these skills to ace Machine Learning Engineer - Financial AI
Machine Learning Engineering
Python
ML Frameworks
Model Deployment
Financial Data Analysis
Collaboration with Engineering Teams
Model Performance Improvement
