DataOps Engineer GSK0JP00107039

Undisclosed Company • City of London, England, GB • 2d ago

At GSK we see a world in which advanced applications of Machine Learning and AI will allow us to develop novel therapies to existing diseases and to quickly respond to emerging or changing diseases with personalized drugs, driving better outcomes at reduced cost with fewer side effects. It is an ambitious vision that will require the development of products and solutions at the cutting edge of Machine Learning and AI, as well as extensive safety and robustness evaluations. We're looking for a highly skilled machine learning engineer to help us make this vision a reality.
Competitive candidates will have a track record of writing and shipping quality, well-documented and well-tested software in the AI/ML industry. We are looking for candidates with experience in the field of Responsible AI, preferably for generative AI or language applications. In addition to ML engineering and data science skills, ideal candidates will demonstrate a keen interest in ethical and safety aspects of using AI in drug discovery and the clinic.
The Responsible AI team is built on the principles of ownership, accountability, continuous development, and collaboration. This fixed-term position is a unique opportunity to contribute to the development and evaluation of generative AI for drug discovery in a fast-paced environment, with a focus on impact and in collaboration with a large team. Our leaders will be committed to your career and development from day one.
Key responsibilities
* Backend development of generative AI evaluations for a Python web application
* Integration of AI/ML components with frontend, backend, data and compute infrastructure
* Responsible for high quality software implementations according to best practices, including automated test suites and documentation
* Participate in code reviews, continuously improving personal standards as well as the wider team and product
* Liaise with other technical staff and data engineers in the team and across allied teams, to build an end-to-end pipeline consuming other data products
Deliver ML- and data-driven insights on GenAI datasets.
Basic qualifications:
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A degree in a quantitative or engineering discipline (e.g., computer science, computational biology, bioinformatics, engineering, among others); OR equivalent work experience as a professional ML engineer or data scientist.
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Strong skills in Python, with experience in backend development and unit testing.
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Knowledge of generative AI, specifically Large Language Models.
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Knowledge of agile practices and able to perform in agile software development environments
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Strong knowledge of modern software development tools / ways of working (e.g. git/GitHub, DevOps tools for deployment) - should be able to show practice of commit early and deploy often
Preferred qualifications:
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Experience with safety and/or robustness evaluations of generative AI, e.g. defining guardrails, red teaming or persona-based evaluations.
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Knowledge of AI/ML approaches and deployment of AI/ML powered applications - especially using language models or NLP and agent-based approaches (e.g. LangGraph, PydanticAI, AutoGen)
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Knowledge of AI/ML evaluation and benchmarking approaches, experience with iterative improvement of AI/ML models and products
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Research experience (e.g., Master's project, internship at research organization).
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Cloud experience (e.g. Google Cloud and cloud run preferred) including core web application infrastructure