What We Are Looking For
We are looking for a Senior DevOps Engineer specializing in AI Platform and MLOps. In this role, your primary focus is the production lifecycle, reliability, and optimization of our core AI products.
This role is not an over-scoped “do everything” position: we are looking for one dedicated owner for the AI production lifecycle, from infrastructure integration to the coding agent runtime.
You have a strong software development background (you’ve built and shipped applications before moving into DevOps/MLOps) and possess the infrastructure literacy needed to be completely autonomous. You will collaborate with the SRE team bringing the necessary GitOps, Kubernetes, and Terraform knowledge to align your AI workloads with the core infra, review code, and lend an extra hand when needed.
This role sits between the Infra and AI teams, reporting to Édouard, while our SRE keeps ownership of the core infrastructure foundation.
You will have strong autonomy on AI tooling and runtime topics, while infrastructure and architecture changes will be validated with our SRE and Édouard.
Key Responsibilities
60% - AI Platform Build & Run
- High-Availability AI Workloads: Design and implement intelligent multi-provider fallback modes to ensure our AI assistant achieves 100% uptime, even during upstream provider outages.
- Inference & Token Optimization: Drive token cost reduction strategies, optimize response latency, and manage the performance of our proprietary, in-house security coding agent.
- LLM Proxy & Routing: Maintain and optimize our custom LLM proxy.
- AI Stack Ownership: Operate and improve our current stack, including AWS Bedrock, LiteLLM, Django web app, and internal dashboard portal.
- LLM Deployment & Orchestration: Own LLM deployment and orchestration topics, including LLM gateways, model serving, inference routing, and runtime reliability.
- AI Observability & Tracing: Build comprehensive request tracing, performance benchmarking, and dedicated alerting pipelines for our models using OpenTelemetry, Prometheus and Grafana.
- Production-Grade Observability: Improve our current observability setup and dashboards to make them cleaner, more actionable, and production-grade.
- AI Feature Lifecycle: Partner closely with our AI engineers to smoothly transition new model capabilities and agents from development into a stable, scalable production runtime.
- Support our AI engineer on coding agent development and deployment.
25% - Infrastructure Integration & SRE Backup
- Infrastructure Literacy & PR Reviews: Keep a pulse on how our infrastructure is built. Actively participate in reviewing infrastructure Pull Requests to ensure seamless alignment with AI platform needs.
- SRE Alignment: Coordinate closely with the SRE team to communicate the architecture and resource requirements of AI workloads, ensuring they fit cleanly into the core EKS setup.
- Kubernetes & Terraform Operations: Use Kubernetes and Terraform to debug, configure, and operate application-level infrastructure.
- Operational Autonomy: Be able to manage the setup independently if our SRE is unavailable.
Our SRE keeps ownership of the infrastructure “socle”, while this role owns deployment and health of the application / AI tooling layers.
15% - General Run & Team Support
- Team-Wide Troubleshooting: Act as a reliable problem-solver for engineering bottlenecks, stepping in to help diagnose and resolve random operational issues across the engineering team.
- Operational Availability: Share the team effort of maintaining overall platform health, updating runbooks, and ensuring development teams have high-performance internal tools.
Out of Scope
- CI, DevTooling, security, compliance, and package updates are not core responsibilities for this role.
- Security, compliance, and package updates belong to the dedicated Security team.
- CI and DevTooling are excluded from the core scope so developers keep ownership of the dev / infra boundary.
Qualifications
- AI/MLOps Mindset: Practical experience dealing with LLM orchestration tools, API latency, token management, and model telemetry.
- Kubernetes & GitOps Knowledge: Solid understanding of Kubernetes concepts and GitOps workflows, not to build them from scratch, but to confidently debug application-level cluster issues and configure environments.
- Terraform Familiarity: Ability to understand and review Terraform codebase setups
- Collaborative Problem Solver: A true team player who enjoys diving into ambiguous, cross-team technical problems to unblock your peers.
- Languages: Professional proficiency in both French and English, with a minimum B2 level required in each.
Nice to Have
- Experience operating production AI / ML platforms.
- Experience with cost optimization for AI workloads.
- Experience with OpenTelemetry, Prometheus, Grafana, or equivalent observability stacks.
- Experience working closely with AI engineers.
- Candidates from startup / scale-up internal product environments.
What Success Looks Like
- After 3 months: deep understanding of the internal AI pipeline, architecture, pain points, and operational risks.
- After 6 months: ability to propose or deliver a stronger multi-cloud / multi-provider AI architecture with better observability and reliability.
Environment
Symbiotic Security is a cybersecurity startup helping developers write secure code through an AI-powered assistant integrated into their IDE and CI/CD pipelines. Our solution has two unique strengths: it provides developers with interactive training to understand vulnerabilities as they code, and it automatically detects and remediates security flaws introduced by generative AI tools such as GitHub Copilot.
Founded in April 2024, we are currently a team of 30 based in Paris and 2 in New York.
Our product team brings together diverse profiles: 8 fullstack engineers, 1 SRE, 2 AI engineers, 4 cybersecurity experts, product managers, and 2 product designers.
The hire will be positioned at the interface between Infra, AI, and Product Engineering, with minimal rituals and a backlog review cadence every 2 weeks.
Why Join Us
- Own a strategic AI production scope at a moment where the company is actively scaling its AI platform.
- Take responsibility for a concrete, high-impact mission: making our AI assistant reliable, observable, and efficient in production.
- Work on a modern AI infrastructure stack combining LLM orchestration, AWS Bedrock, LiteLLM, Kubernetes, Terraform, observability, and multi-provider routing.
- Have a high level of autonomy to propose better architectures and tooling, while collaborating closely with experienced Infra, AI, and Product Engineering teams.
- Join an early-stage cybersecurity startup where the AI platform is directly connected to the product’s core value and developer experience.
Benefits
- 💰 70K to 100K€ and equity options (BSPCE) from an early stage startup.
- 🩺 Health insurance fully covered.
- 🧑💻 €500 equipment budget to set up your ideal workspace.
- 🍽️ Swile meal card for lunch breaks.
- 🌴 RTT days on top of standard paid vacation + €250 Holiday allowance.
- 🏡 Remote-friendly: up to 2 days/week for people living near Paris.
- 🥂 Vibrant culture with regular team outings and a €2000 referral bonus.
Hiring Process
- Talent Acquisition Call (30 min - Visio)
- Live Technical Test (1,5 hours - visio)
- Meet Paul our SRE (30 min visio) and Edouard our CTO (30 min visio)
- Team Fit (Lunch or drink with the team)
- Formal Proposal