ORGANIZATION: Global Digital Technologies ABOUT THE ROLE We are looking for 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 • PhD in Computer Science, Mathematics, Engineering, or a related field preferred; outstanding candidates with a Master's degree will also be considered. All candidates must have hands-on experience in building multi-agent systems and/or 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