Causaly
Causaly accelerates how humans acquire knowledge and develop insights in Biomedicine.
Artificial Intelligence • Natural Language Understanding • Knowledge graphs • Pharma • Life Sciences
51 - 200
MLOps Engineer
June 11
🔄 Hybrid – London
Causaly
Causaly accelerates how humans acquire knowledge and develop insights in Biomedicine.
Artificial Intelligence • Natural Language Understanding • Knowledge graphs • Pharma • Life Sciences
51 - 200
Description
• The ML Ops Engineer will be responsible for designing, developing, and maintaining the infrastructure and tools that support our machine learning models • Design, implement, and maintain our ML infrastructure, including data pipelines, model training, and deployment workflows • Develop and maintain tools for automating ML workflows, such as data pre-processing, feature engineering, and model evaluation • Collaborate with stakeholders to optimize model performance, scalability, and reliability in production, including monitoring, logging, and troubleshooting • Develop and maintain data quality checks and data validation pipelines • Implement and maintain data versioning and data lineage tracking • Stay up-to-date with the latest developments in ML Ops and recommend best practices and new technologies to the team
Requirements
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field • Applied industry experience in MLOps, DevOps, or a related field • Excellent programming skills in Python, with experience in ML frameworks • Experience with containerization • Experience with data pipelines, data warehousing, and ETL processes • Experience with data versioning and data lineage tracking • Strong understanding of ML model deployment, scaling, and management • Excellent problem-solving skills, with the ability to debug complex issues • Strong communication and collaboration skills, with the ability to work with cross-functional teams • Experience with agile development methodologies and version control systems such as Git
Benefits
• Competitive compensation package • Private medical insurance (underwritten on a medical health disregarded basis) • Life insurance (4 x salary) • Individual training/development budget through Learnerbly • Individual wellbeing budget through Juno • 25 days holiday plus public holidays and 1 day birthday leave per year • Hybrid working (home + office) • Potential to have real impact and accelerated career growth as an early member of a multinational team that's building a transformative knowledge product
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