AI Systems / Agent Engineer- SOUTH AFRICA (REMOTE)

Darum lohnt es sich

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. • 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 • 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 • 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. 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.