Infrastructure & DevOps Engineer

Delta Labs uses AI to simulate and predict consumer behaviour at scale. We build Elaiia, a simulation engine that generates AI Twins — intelligent synthetic agents that mirror real consumer populations. Our clients use Elaiia to simulate customer decisions before committing to them: pricing strategies, product launches, campaign messaging, channel allocation. We replace surveys, focus groups, and intuition with simulation-based evidence.

We’re a small, focused team and we intend to stay that way. We give people ownership, trust, and the autonomy to do their best work. We work with urgency and expect new team members to match our pace. Elaiia is live, paying customers use it daily, and the problems ahead are about scaling what works — not figuring out if it works.

What You’ll Do • Own the reliability, performance, and observability of Elaiia’s infrastructure on Azure • Build and maintain the instrumentation that keeps us informed — alerting, logging, error tracking (Sentry, Slack integrations, etc.), wired directly into the codebase, not bolted on from outside • Manage and optimise how we scale LLM workloads: context management, parallel execution, rate limiting, cost control. Our scaling challenge isn’t handling millions of HTTP requests — it’s orchestrating many concurrent LLM calls efficiently • Manage CI/CD pipelines, deployment processes, and cloud resources • Work in the codebase to connect infrastructure tooling to the application layer — you’re not handing off tickets to engineers, you’re writing the integration yourself • Identify and resolve infrastructure bottlenecks, working with the simulation and data teams to understand where latency, throughput, and cost matter most • Own security fundamentals: access controls, secrets management, and vulnerability awareness

What You Need • 3+ years in infrastructure or DevOps roles — you’ve operated production systems, debugged under pressure, and built things that stay up • You can write production-quality code, not just scripts. You’ll work inside the codebase to wire in observability, alerting, and integrations. You don’t need to be a software engineer, but you need to be comfortable reading, navigating, and contributing to a Python codebase • Solid experience with Azure cloud services • Understanding of LLM-specific infrastructure challenges: API rate limits, context windows, token costs, parallel execution patterns. This isn’t traditional web-scale — if your instinct is to add more pods, this role will surprise you • You use AI tools (coding agents, search, automation) as part of your daily workflow — not as a novelty, but as a multiplier • Good understanding of networking, DNS, TLS, and latency diagnostics • Experience with monitoring and observability tooling (Sentry, Datadog, Grafana, or similar)

Nice to have • Experience managing LLM API costs and optimising inference pipelines • Background working in small teams where you wore multiple hats • Familiarity with Python async patterns and concurrency