AI Automation Engineering, GitGuardian

• 5+ years in a technical or solutions role (Analytics Engineering, Solutions Engineering, Data Analysis, or technical Business Ops) with a track record of production automations or tools
• Python expert for APIs, data manipulation, and robust scripts/apps
• Hands-on with Generative AI (Dust, RAG, agentic workflows, prompt engineering)
• Pragmatic: use no/low-code (n8n/Zapier/Make) when fastest; drop to Python when it matters
• Product mindset for internal tooling: intuitive interfaces, clear UX, adoption across non-technical teams
• Strong stakeholder management and communication; collaborate across technical and non-technical teams
• Familiar with data warehousing (Snowflake) and SQL to fuel automations

Desirable

• As our first dedicated AI Automation Engineer, you will be a foundational part of our data team's expansion
• You will join a core team of 3 Data Engineers, and will be one of two new dedicated roles (alongside an Analytics Engineer) being added to build out our advanced data capabilities
• You will be pivotal to GitGuardian's growth and success by establishing and leading our internal AI and automation function, acting as a force multiplier for the entire company
• You will design, build, and deploy an internal AI platform—automations, agents, and data-driven workflows—that other teams adopt and extend
• You’ll sit at the crossroads of Business Process, AI Application, and Data, directly improving how GitGuardian operates at scale
• Design and Build Business Automations: Identify, design, and implement robust workflows connecting systems (HubSpot, Notion, Linear) to eliminate manual processes using n8n or Python
• Build Internal Apps & Agents: Develop Dust-powered agents and wrap them in lightweight interfaces (simple UIs, CLIs, or chat) for self-serve usage and strong adoption
• Fuel with Data: Partner with Analytics and Data Engineers to leverage trusted models and pipelines—ensuring quality, observability, and fast iteration
• Strategic Partner: Proactively map processes, spot high-impact opportunities, and translate needs into scalable, user-friendly solutions
• Centre of Excellence: Set paved paths, best practices, and manage our AI & Automation tooling (Dust, n8n) to enable teams to ship safely and fast
• Own the Lifecycle: Discovery to production, documentation, enablement, monitoring, and iterative improvement focused on reliability, UX, and adoption
• Stay at the Forefront: Bring pragmatic innovation from agentic workflows, retrieval, and evaluation into production—not just experiments
• Technical Environment:
• Core Languages: Python, SQL
• AI: Dust, LangChain
• Automation: n8n
• Data Warehouse: Snowflake
• Orchestration & Deployment: Dagster, Kubernetes, Docker, Terraform
• Data Ingestion: Fivetran, Airbyte, custom scripts
• Data Sources: PostgreSQL, Elasticsearch, various APIs (Hubspot, Notion, etc.)

• Interview with your future manager
• To know more about yourself, your professional experiences and your motivations
• Also to assess end-to-end delivery of automations/agents; stakeholder communication; pragmatic tool choice (no/low-code vs. Python); familiarity with Dust/LLM platforms
• Technical interview
• To evaluate your skills for the position and project yourself into the role
• We will assess practical automation engineering, AI agent design, and data integration
• Final interview with an Executive Manager
• To detail our company’s vision and ambitions for the next couple of years
• We will assess your product mindset, change management, and enablement of non-technical users
• References check
• You can start thinking about two contacts who can attest to your previous or current professional experiences
• These contacts should be as recent as possible, and we will call them at the end of the process