Senior Machine Learning Engineer - London / Hybrid - up to £100,

Ever wondered about the Machine Learning underpinning a leading fintech app? You're onto something big! While our Machine Learning involvement is currently modest, our company is embarking on an exciting journey to enhance customer experiences and outcomes through personalized solutions. This involves leveraging a range of models, including recommendation ML and GenAI, to tailor various content, touchpoints, and customer interactions.

As a Senior ML Engineer with us, you'll play a pivotal role in constructing foundational architecture and modeling capabilities crucial for our upcoming growth phase. Collaborating closely with data scientists, you'll transition their prototypes into production, strategize deployment patterns for cost-effective customer service, and bolster our broader cloud application systems.

Within our lean team culture, akin to how data scientists support their engineering counterparts, we anticipate a fraction of your time assisting with aspects of data science workflows. And fear not, work-life balance remains paramount.

We foresee much of our infrastructure leveraging ML Flow. Yet, beyond that, we're open to diverse patterns and frameworks of your choosing.

Responsibilities:

- Championing efficient model deployment and training governance

- Operationalizing data scientist prototypes

- Fine-tuning training and deployment for optimal cost/performance ratios

- Collaborating with data scientists to devise transfer learning solutions for sluggish learning rates

- Monitoring deployed training and inference architectures for sustained performance

- Contributing insights to decision science and Data Strategy for enduring ML success

Who We Seek:

- Proven experience in managing ML systems at scale, serving millions of users

- Passion for optimization and a penchant for refining problem-solving approaches

- Systems thinking adeptness for scalable solutions within nascent systems

- Thrive in fast-paced environments typified by startup dynamics

- Curiosity and adaptability to absorb new knowledge and challenge existing frameworks

- Comfort with ambiguity – an attribute that's rare in this specific role

Essential Experience and Skills:

- 3+ years of industry experience overseeing ML models in customer-facing production

environments

- Minimum 1 year of end-to-end development of a productionized ML system

- Proficiency in setting up model-serving APIs for seamless collaboration with engineering teams

- Applied machine learning expertise, including model tuning and evaluation

- Experience navigating cost/performance optimization in ML

- Familiarity with feature stores for signal consolidation and training

- Demonstrated use of robust governance for model tracking, versioning, and training

Bonus Experience:

- Deployment proficiency with Databricks, Azure, or ML Flow

- Experience deploying open-source LLMs in customer-facing settings

- End-to-end ML system setup, encompassing governance and architecture decisions

Join us, where innovation meets impact, and your expertise fuels our journey towards customer-centric excellence!