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Required Senior AI Platform Engineer – Sovereign AI Engineering
The Dream Job
It starts with you – an engineer driven to build the agentic AI platform that turns LLMs into reliable, production-grade capabilities. You care about clean APIs, well-defined service boundaries, and systems that teams can build on with confidence. We are AI-first across the board – every team builds and operates agents. You'll architect and ship the platform that makes this possible: agent orchestration frameworks, LLM gateways, evaluation pipelines, tool-calling infrastructure, and retrieval systems. Without this platform, agents don't ship – you own the layer that turns AI research into Sovereign AI products, deployed across cloud and on-prem environments.
If you want to make a meaningful impact, join our mission and build the agentic AI platform that drives Sovereign AI products – this role is for you.
Responsibilities
Design and build agentic systems – single and multi-agent workflows with planning, memory, context engineering, and tool use – for both internal automation and product-facing autonomous capabilities operating over long time horizons.
Build and operate the AI platform layer – LLM gateways, prompt management, structured output handling, tool-calling infrastructure, and cost/latency optimization – deployed on Kubernetes, consumed by every team for their agentic work.
Own the agent framework layer – orchestration primitives, execution environments, state management, and sandboxed tool execution – giving every team the building blocks to create and operate their own agents.
Build evaluation infrastructure that gives teams confidence in agent behavior – automated LLM and agent evals for quality, correctness, safety, latency, cost, and regressions, including human-in-the-loop oversight for mission-critical workflows.
Productionize and harden backend services (APIs, gRPC, async workers) that integrate LLMs – with proper error handling, retries, circuit breakers, and high-availability patterns.
Own RAG pipelines and retrieval systems – indexing, chunking, embedding, vector database management, filtering, and relevance tuning for production retrieval.
Optimize performance and cost across the AI stack – model routing, caching, batching, and inference cost management.
Ship shared tooling – libraries, SDKs, agent templates, and documentation – while working closely with ML Platform, Data Platform, DevOps, and other teams across the Applied AI Engineering group. Own architecture, documentation, and operations end-to-end.