AI Engineer

About GitGuardian GitGuardian is a global cybersecurity scale-up. The company is based in Paris, New-York City, Boston. Among our early investors who saw our market value proposition, are the co-founder of GitHub, Scott Chacon, along with Solomon Hykes , Docker's co-founder. American and European top-tier VC firms have also invested in GitGuardian. GitGuardian leads the way in Non-Human Identity security , offering end-to-end solutions from secrets detection in code, productivity tools and environments to strong remediation, observability and proactive prevention of leaks. Our solutions are already used by more than 600K developers worldwide! About your team and your mission You'll join the Incidents Squad, the team responsible for the full lifecycle of a GitGuardian incident — from detection to remediation. AI models are involved at every stage, and the team both implements and maintains them. As a Senior Software Engineer focused on AI/LLM features, you'll be brought in to improve, scale, and stabilize these capabilities as they transition from early-stage features to core product workflows. In your first six months, you'll get fully acquainted with the existing LLM features, bring your expertise to improve their reliability and stability (quality, latency, robustness, observability), and start suggesting and driving product improvements in close collaboration with the team. On a day-to-day basis, you will: Build and iterate on LLM features, including agentic workflows, using LangGraph and LangSmith. Scale the platform foundations behind these features — orchestration, performance, and reliability. Partner closely with the team's ML Engineer on design, evaluation, and productionization of AI systems. Why this role Own the AI at the heart of GitGuardian's core product. The Incidents Squad owns the full lifecycle of a secret leak — creation, prioritization, remediation — and AI is embedded at every step. You'll be the engineer making it work reliably at scale. Work at the intersection of product and ML. You'll collaborate directly with a dedicated ML Engineer on design, evaluation, and productionization, bridging the gap between research and production. Be at the frontier of applied AI. As GitGuardian ships more and more LLM-based features, you'll be the person improving their reliability, latency, and robustness as they become core product workflows. High ownership, senior scope. This is a senior role with real influence on architecture and product direction. Technical Environment Core Languages: Python, SQL AI: Dust, LangChain, LangGraph, LangSmith 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.) About you If you think you match at least 70% of these criteria, please apply! Here's what we consider essential for success in this role: You have 5+ years of experience in software engineering and a proven track record of shipping production systems. You have hands-on experience building LLM-powered features in production, with a clear understanding of the trade-offs (quality, cost, latency, safety) and how they connect to product and business needs. You know how to systematically improve LLM systems: versioned prompts, offline/online evaluations, data analysis, and iteration based on real-world signals. You can industrialize LLM systems at scale (caching, batching, model routing, observability, rate limiting, ML CI/CD pipelines). You are strong in Python and comfortable working with REST APIs and making pragmatic architectural decisions. You operate as a senior engineer: debugging complex systems, refactoring with intent, reviewing code thoughtfully, and thinking ahead about scalability. You take ownership: you identify gaps, propose solutions, and go beyond your immediate scope. You connect technical decisions to product and business outcomes.