It runs applied research (ML and agentic/LLM systems, planning, simulation and evaluation), rapid prototyping and production engineering (APIs, CI/CD, observability, data platforms and cost/latency optimisation), and rigorous evaluation and safety work (benchmarks, red-teaming, monitoring, governance and compliance). * Build and maintain reliable AI-enabled backend systems, including orchestration layers, tool integrations, knowledge access mechanisms, and observability. * Design modern knowledge access architectures for LLM systems, including retrieval strategies, semantic indexing, and structured interfaces to enterprise data sources. * Apply strong software engineering practices (testing, CI/CD, monitoring, structured logging, versioning of prompts and models) to ensure reliability and maintainability of AI systems. * Contribute to system architecture decisions including agent ...
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