MLOps Engineer

Job Description

About the Role

Title: MLOps Engineer

Location: London England GB

Job Description:

About us

Founded in 2018, Causaly accelerates how humans acquire knowledge and develop insights in Biomedicine. Our production-grade generative AI platform for research insights and knowledge automation enables thousands of scientists to discover evidence from millions of academic publications, clinical trials, regulatory documents, patents and other data sources in minutes.

We work with some of the world’s largest biopharma companies and institutions on use cases spanning Drug Discovery, Safety and Competitive Intelligence. You can read more about how we accelerate knowledge acquisition and improve decision making in our blog posts here: Blog – Causaly

We are backed by top VCs including ICONIQ, Index Ventures, Pentech and Marathon.

About the role:

The ML Ops Engineer will be responsible for designing, developing, and maintaining the infrastructure and tools that support our machine learning models. You will work closely with our data scientists, engineers, and product teams to ensure the smooth operation of our ML workflows, from data ingestion to model deployment.

Responsibilities:

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

Preferred Qualifications:

Experience with MLOps platforms such as MLflow, TensorFlow Extended (TFX), or Kubeflow

Experience with DevOps tools such as Jenkins, GitLab CI/CD, or CircleCI

Experience with BigQuery

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

Be yourself at Causaly… Difference is valued. Everyone belongs.

Diversity. Equity. Inclusion. They are more than words at Causaly. It’s how we work together. It’s how we build teams. It’s how we grow leaders. It’s what we nurture and celebrate. It’s what helps us innovate. It’s what helps us connect with the customers and communities we serve.

We are on a mission to accelerate scientific breakthroughs for ALL humankind, and we are proud to be an equal opportunity employer. We welcome applications from all backgrounds and fairly consider qualified candidates without regard to race, ethnic or national origin, gender, gender identity or expression, sexual orientation, disability, neurodiversity, genetics, age, religion or belief, marital/civil partnership status, domestic / family status, veteran status or any other difference.