Key Accountabilities
- Design and delivery of AI‑enabled application components and agent workflows
- Reliability, performance, and operational supportability of deployed solutions
- Adherence to enterprise engineering standards, security controls, and data governance requirements
Principal Responsibilities
- Design, build, and test multi‑step agent workflows using established patterns such as ReAct, planner‑executor, and tool‑chaining
- Integrate LLMs (i.e. Claude and OpenAI) with enterprise APIs and internal systems, including robust handling of retries, edge cases, and degraded states
- Implement tool calling, function orchestration, and compensating actions to ensure workflows remain stable under failure conditions
- Design and implement human‑in‑the‑loop controls, including approvals, escalations, and exception handling where required by business logic or risk considerations
- Collaborate with product managers, domain experts, and technical stakeholders to refine requirements and translate them into technical designs
- Develop, maintain, and version prompt logic and supporting documentation in line with agreed standards
- Build and manage agent memory and retrieval mechanisms using vector databases and retrieval‑augmented generation (RAG), ensuring context remains relevant and high‑signal
- Deploy AI services to production environments and actively monitor logs, traces, and metrics to detect and resolve issues
- Contribute to test coverage, operational runbooks, and incident response practices in collaboration with QA and operations teams
- Support continuous improvement of system reliability, performance, and maintainability