Machine Learning Engineer, London (5 days a month), £90,000 - £110,000 per annum My client, a leading regulatory intelligence company are currently looking to bring on an ML Engineer on a permanent basis. This is a key hire for the business and will look to move quickly for the right candidate.

They are based in Central London and you will be required in their London office 5 days per month.

As ML Engineer, your mission is to: Participate in the continuous improvement of our client's products Develop advanced NLP and AI-based products that will delight users Provide excellence in cloud-based ML engineering, with as much focus on Operations as Development. Expand of the Team’s knowledge via demonstration and documentation

Key Responsibilities As a machine learning engineer, your main responsibility is to conduct the development and productionisation of ML and NLP-based features for the client's products - a SaaS Platform and an API. Develop optimal ML & NLP solutions for use cases, from baseline to SOTA approaches, wherever appropriate. Produce high quality, modular code, and deploy following our established DevOps CI/CD and best practices. Improve the efficiency, performance, and scalability of ML & NLP models (this includes data quality, ingestion, loading, cleaning, and processing). Stay up-to-date with ML & NLP research, and experiment with new models and techniques. Perform code-reviews for your colleague’s code. Engage with them to raise standards of Software engineering. Propose cloud architectures for ML-based products that need new infrastructure. Participate in the monitoring and continuous improvement of existing ML systems.

Core requirements Experience matters. But what is more important than raw number of years of experience is demonstrated proficiency (through GitHub profiles/online portfolios and the interview process itself). Bonus points for Stack Overflow and Kaggle contributions!

What we are looking for Experience analysing large volumes of textual data (almost all of our use cases will involve NLP) Ability to write clear, robust, and testable code, especially in Python Familiarity with SQL and NoSQL/graph databases Extensive experience with ML & DL platforms, frameworks, and libraries Extensive experience with end-to-end model design and deployment within cloud environments ☁️ A systems thinking approach , with passion for MLOps best practises An engineer that can think in O(n) as much as plan the orchestration of their product. Solid understanding of data structures, data modelling, and software architecture, especially cloud-based. ️ An engineer that can keep up with mathematically and statistically-oriented colleagues A healthy sense of humour.

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