AI Engineer, Verisian

• Experience with and foundational understanding of LLMs (especially open source models), including production deployment
• Experience with and foundational understanding of non-LLM AI, including production deployment
• Experience with RAG systems (Pinecone or similar)
• Strong interest in programming languages, parsing algorithms, interpreters, and compilers
• Extensive experience in Python, Pandas, and at least one other programming language
• Experience with and clear understanding of graph theory and algorithm implementation
• Highly motivated and highly independent, able to create and manage a code base
• We & mission mindset
• Iteration mindset
• Strong focus on building as a team
• Strong communication skills
• “If it’s not code and documented, it doesn’t exist”-mindset

Desirable

• As an AI Engineer, you will join our world-class engineering team in building the Verisian Platform
• You will use the latest in AI, LLMs, and RAG systems and combine them with code and data traceability, the core of Verisian’s technology, to create the most reliable and accurate code generator to analyze clinical trials
• You will be expected to lead the analysis, design, engineering of the AI stack, including its RAG system, selection and fine-tuning of models, as well as its production-ready deployment
• As part of our core team, you will join us in designing, prioritising, building and testing new functionality, troubleshooting customer issues, finding root causes, and developing improvements to ensure maximal user impact and performance
• Our RAG system is based on Python and Pinecone and we have deployed a set of open source models
• We interact with our code and data traceability graph through our main application stack which is currently based on Next.js and deployed via Docker/Kubernetes in the cloud
• Our code analysis pipelines are based on Antlr4 and Java
• Git is where our code lives, and Github Actions is how it gets out into the world
• Your role will be to build the Verisian Agentic AI Scientific Computing Environment (SCE)
• It will resolve issues identified in our Validator, modify existing code, and create analyses from pre-existing templates and eventually from scratch
• It does so by checking out code using git, modifying it using our full study traceability insights (specifications, metadata, data, and code traceability), feeding its results back into the Verisian Platform, and ultimately reaching its goals through iterations guided by expert-reviews using the Verisian Validator
• The Agentic AI SCE will increase the speed with which clinical trials are analyzed and become a crucial part of the Verisian Builder
• In combination with the Verisian Validator that extracts full dataset and column-level data lineages and dependencies from existing clinical trials, the Verisian Platform will allow for the efficient creation, validation, understanding, modification, communication of clinical trials and enable code and data reuse
• Clinical trials will be analyzed and validated faster, better, and at lower costs to the benefit of all of humanity