Founding AI Platform Engineer (MLOps / Backend)

Are you ready to take your MLOps skills to the next level? We are hiring a Founding AI Platform Engineer to own the systems that make our ML and GenAI products reliable, deployable, observable, and scalable. This role sits at the intersection of backend engineering, infrastructure, MLOps, and product delivery. You will build the production layer around training, evaluation, deployment, serving, CI/CD, experimentation, and monitoring. In a team of our size, this role spans backend services, infrastructure, tooling, and reliability work. Your job is to make sure promising ML and GenAI capabilities become stable, customer-ready systems. All you need is: - Strong software engineering background with experience building and operating production systems; - Experience with backend services, cloud infrastructure, CI/CD, testing, observability, and automation; - Strong Python skills and comfort working across services, tooling, infrastructure, and operational workflows; - Good judgment about reliability, performance, maintainability, and cost tradeoffs; - Ability to collaborate closely with ML and product teams and move ambiguous work to completion; - High ownership, attention to detail, and a bias toward simplifying and strengthening systems. What would be an advantage: - Experience with MLOps workflows for model training, evaluation, deployment, and monitoring; - Experience serving ML models or LLM applications in production; - Experience with experimentation platforms, event pipelines, analytics instrumentation, or feature delivery platforms; - Experience with agent evaluation, prompt versioning, retrieval/search infrastructure, or vector-backed systems; - Experience supporting customer-facing APIs or SaaS platform infrastructure. Your daily adventures will look like: - Build and maintain the infrastructure and tooling used to train, evaluate, deploy, and monitor ML models and GenAI services; - Own production services, APIs, and pipelines that power recommendations, agent workflows, and customer-facing integrations; - Improve CI/CD, testing, release workflows, rollback processes, and environment management; - Establish observability across service health, model behaviour, agent quality, latency, cost, and failure modes; - Build reproducibility and lifecycle practices for models, prompts, datasets, configurations, and releases; - Support experimentation and measurement infrastructure so product and ML changes can be evaluated cleanly; - Improve reliability, scalability, security, performance, and cost efficiency across the stack; - Troubleshoot production issues end-to-end and turn recurring pain points into durable engineering improvements; - Help define the platform and engineering standards the company will rely on as it grows. What Success Looks Like in the First 6 Months: - Shipping a model or GenAI change to production becomes faster, safer, and less manual; - Core services and AI workflows are observable and easier to debug; - The platform supports more usage with better reliability and lower operational friction; - Engineers spend less time fighting infrastructure and deployment issues and more time shipping product; - You become the person who can see platform, reliability, and scaling risks early and address them before they become problems. And this is how our interview process goes: - A 30-minute interview with a member of our HR team to get to know you and your experience; - A 1-hour technical interview; - A final interview to gauge your fit with our culture and working style. Sounds interesting? Do not hesitate to apply or contact us if you have any questions!