2026届Agentic AI Engineer (MJ009005)

ORGANIZATION: Global Digital Technologies ABOUT THE ROLE We are looking for Agentic AI Engineers who can design, build, and deploy autonomous agentic systems that operate reliably in production environments. This is not a prompt-engineering role This is not a research-only role You will architect, build and deploy end-to-end: • Agentic systems that perform complex workflows, e.g. plan and execute multi-step tasks • Tool-using LLM systems (retrieval, function calling, code execution, browsing, etc.) • Multi-agent orchestration frameworks • Long-term memory architectures (vector + structured state) • Evaluation harnesses for reliability, hallucination detection, and safety • Production inference pipelines (latency, scaling, observability) • Guardrails and fallback systems RESPONSIBILITIES • Design and build production-grade agentic AI systems end-to-end, including planning and execution loops, stateful memory, secure tool invocation, sandboxed execution, and robust retry and reflection mechanisms • Deploy and operate scalable inference infrastructure optimized for latency and cost, with strong observability through logging, tracing, evaluation metrics, and proactive monitoring for drift and failure modes • Establish rigorous reliability and safety frameworks by developing automated evaluation pipelines, product-aligned benchmarks, and stress tests for reasoning performance. • Implement guardrails and governance controls, including constraint systems, hallucination mitigation, permissioning, audit trails, and human-in-the-loop workflows to ensure secure and dependable real-world operation. QUALIFICATIONS • Bachelor’s degree or above in Computer Science, Mathematics, Engineering or related field, with hands-on experiences building multi-agent systems or/and conducting research in LLM, agents or related areas • Strong hands-in proficiency with AI-native development tools such as Claude Code, Cursor or comparable AI coding assistants, with demonstrated ability to use them to accelerate SDLC • Deep understanding of tool calling / function calling, RAG architectures, Vector databases, Prompt chaining vs planner/executor models, Latency optimization and cost tradeoffs • Experience deploying cloud-native systems • Familiarity with observability stacks (metrics, tracing, logging) and production monitoring practices • Contributors to open-source AI tooling are strongly preferred • Example tech stack: Python, FastAPI, LLM APIs and open-weight models, Vector databases, Kubernetes /serverless deployment