Founding AI Platform Engineer (MLOps / Backend)

• 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.

• 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.