AI Systems

Il y a 3 semaines

, 00, Maroc Cliead Télétravail Temps plein
Mission: Build the engineering harness that allows a small specialist model to operate reliably as an end-to-end software engineer. Responsibilities Design and build the agent/harness architecture around specialist models. Build repository understanding and codebase retrieval. Connect models to Git, compilers, debuggers, test runners and development environments. Build planning → execution → testing → evaluation → correction loops. Integrate architecture/dependency analysis. Integrate security and static-analysis tooling. Build automated performance/load-testing workflows. Design model verification and critic/evaluator systems. Develop tool-use and function-calling infrastructure. Build mechanisms that prevent models from modifying code before understanding the task/context. Develop observability and evaluation infrastructure for agent behavior. Determine which capabilities should be handled by the model and which should be deterministic system components. Work closely with the Model Research Engineer to feed real-world failures back into model training. Ideal background Strong AI agent / ML systems engineering experience. Experience building production coding agents or developer tools. Strong Python/TypeScript and backend engineering. Experience with tool calling, orchestration and agent frameworks. Strong understanding of software architecture. Experience with RAG, code retrieval and knowledge graphs. Familiarity with Docker, Kubernetes, CI/CD and cloud infrastructure. Experience with evaluation and observability systems. Bonus Compiler/toolchain experience. Static analysis. Code security. Performance engineering. SWE-bench or repository-level coding agents. LangGraph, Claude Agent SDK, OpenAI Agents SDK or similar. What success looks like Take a small specialist model and build a system around it that allows it to reliably understand, plan, implement, test, debug and validate real software-engineering tasks.