Job Summary
Veeva Platform AI Activation Engineer
• Demonstrated, evidenced delivery of AI-enabled tools or ways of working that produced a measurable efficiency gain — candidates must be able to walk through at least one such initiative end to end: the problem, what was built or changed, who adopted it, and the benefit realised. This is the primary screening criterion and outweighs total years of experience.
• Solid software engineering foundation with strong hands-on Python — able to build, ship and maintain a real internal application, not only notebooks or prototypes.
• Hands-on experience building LLM-based applications: API integration, prompt design and iteration, retrieval-augmented generation, embeddings and vector/semantic search, chunking strategies, evaluation of output quality, and controlling cost/latency.
• Practical experience with data-centric application work: relational data (SQL or equivalent), structured file ingestion and parsing (Excel/CSV/XML/JSON), data modelling and validation.
• Experience with a Python web/data application framework (Streamlit, Dash, FastAPI or equivalent) and standard engineering discipline: Git/GitHub, code review, environment management, deployment to a managed hosting platform.
• Demonstrated ability to work alongside non-AI practitioners and change how they work — observing an existing process, identifying where AI genuinely helps, coaching individuals, and getting real adoption rather than delivering a demo.
• Awareness of AI risk and governance in an enterprise setting: data privacy and confidentiality, hallucination and output-verification risk, human oversight, model/prompt change control, and Responsible AI principles.
• Willingness and ability to work within a regulated (GxP/validated) environment — comfortable with documentation, traceability and change control as a normal cost of delivery rather than an obstacle.
• Experience in Agile/Scrum delivery (Jira-based backlog, sprint-level delivery).
• Strong English communication skills — this role interfaces directly with internal Leads, platform practitioners and business stakeholders, and must explain AI capability and its limits in plain language.
• REQUIRED - Veeva Vault literacy, or demonstrated ability to reach it within the first 8 weeks: the object and metadata model, configuration exports and reports, VQL, and the Vault REST API. The role will be cross-trained in Veeva by the CoE. This literacy is needed to build AI over Vault configuration and documentation, and to assess Veeva-native AI features credibly - it is NOT intended to make this role a source of configuration or integration delivery capacity.
Key Responsibilities
Veeva Platform AI Activation Engineer
• Demonstrated, evidenced delivery of AI-enabled tools or ways of working that produced a measurable efficiency gain — candidates must be able to walk through at least one such initiative end to end: the problem, what was built or changed, who adopted it, and the benefit realised. This is the primary screening criterion and outweighs total years of experience.
• Solid software engineering foundation with strong hands-on Python — able to build, ship and maintain a real internal application, not only notebooks or prototypes.
• Hands-on experience building LLM-based applications: API integration, prompt design and iteration, retrieval-augmented generation, embeddings and vector/semantic search, chunking strategies, evaluation of output quality, and controlling cost/latency.
• Practical experience with data-centric application work: relational data (SQL or equivalent), structured file ingestion and parsing (Excel/CSV/XML/JSON), data modelling and validation.
• Experience with a Python web/data application framework (Streamlit, Dash, FastAPI or equivalent) and standard engineering discipline: Git/GitHub, code review, environment management, deployment to a managed hosting platform.
• Demonstrated ability to work alongside non-AI practitioners and change how they work — observing an existing process, identifying where AI genuinely helps, coaching individuals, and getting real adoption rather than delivering a demo.
• Awareness of AI risk and governance in an enterprise setting: data privacy and confidentiality, hallucination and output-verification risk, human oversight, model/prompt change control, and Responsible AI principles.
• Willingness and ability to work within a regulated (GxP/validated) environment — comfortable with documentation, traceability and change control as a normal cost of delivery rather than an obstacle.
• Experience in Agile/Scrum delivery (Jira-based backlog, sprint-level delivery).
• Strong English communication skills — this role interfaces directly with internal Leads, platform practitioners and business stakeholders, and must explain AI capability and its limits in plain language.
• REQUIRED - Veeva Vault literacy, or demonstrated ability to reach it within the first 8 weeks: the object and metadata model, configuration exports and reports, VQL, and the Vault REST API. The role will be cross-trained in Veeva by the CoE. This literacy is needed to build AI over Vault configuration and documentation, and to assess Veeva-native AI features credibly - it is NOT intended to make this role a source of configuration or integration delivery capacity.