Implement solutions using modern development approaches including low-code platforms, AI-assisted development environments and agentic coding workflows where appropriate
Build and operate monitoring and observability for ML systems (data drift, model performance, system health), and define alerting/incident response processes.
Conduct needs analyses and evaluates the effectiveness of AI-enhanced teaching approaches, making data-driven recommendations for continuous improvement.
Domain expert: You’ve scaled AI products in production and design for LLM failure modes such as hallucination, context degradation, prompt injection and latency under load.
Agentic AI architecture: Design the end-to-end architecture for FAM agentic AI solutions, including agent orchestration, workflows, state management, tool integration, human approval points and system interactions. Use suitable frameworks such as LangGraph or CrewAI. Architecture decisions will be made in consultation with DSAD’s Platform Team.
Collaborate with researchers and engineers to define research directions, develop prototypes, and translate research outcomes into practical solutions for real-world technical and business challenges.
Hands-on experience building and deploying ML/AI applications, including at least one LLM-enabled or agentic AI solution beyond proof-of-concept stage.