AI Automation / Application Engineer

Design and Deploy AI Applications for Smarter Decision-Making

Join a dynamic team focused on building innovative AI solutions that streamline business operations. In this role, you will work closely with cross-functional teams to design, develop, and maintain tools that automate processes, enhance decision-making, and improve efficiency. This position offers the opportunity to create impactful solutions while collaborating with experts in data science, IT, and compliance.

Responsibilities

• Develop and maintain AI skills and agents to automate repetitive tasks such as report generation, document checks, and workflows.
• Create interactive dashboards using R Shiny or Shiny for Python, incorporating AI assistants to provide insights and flag issues.
• Automate manual processes across business tools, documents, and spreadsheets.
• Build supervised automations where outputs are reviewed and approved by users before implementation.
• Design and implement data pipelines and integrations to ensure reliable insights from business systems.
• Write tested, version-controlled code and produce clear documentation for tools and processes.
• Collaborate with senior engineers and compliance teams to validate tools for regulated use cases.
• Conduct user testing, provide training, gather feedback, and iterate on solutions to improve adoption and usability.
• Maintain and troubleshoot live tools, ensuring functionality as source systems evolve.

Key Skills and Requirements

• Proficiency in Python for scripting, automation, and data processing (e.g., pandas, requests).
• Experience with R and Shiny for developing and deploying applications.
• Strong SQL skills and familiarity with integrating systems via REST APIs.
• Practical experience with generative AI tools, including LLM APIs and prompt design.
• Familiarity with Git and version control, and ability to work iteratively with non-technical users.
• Experience with cloud data warehouses like Snowflake.
• Knowledge of building AI agents, agent skills, or connectors to business systems.
• Familiarity with agent frameworks such as LangGraph.
• Proficiency in business intelligence and data preparation tools.
• Experience deploying applications and scheduled jobs using containers (e.g., Docker) in a cloud environment like AWS.
• Background in pharmaceutical R&D or other regulated environments (e.g., GxP).
• A portfolio or GitHub profile showcasing relevant work is a plus.