What are the responsibilities and job description for the Forward Deployed Engineer position at SIERRA AI?
Serve as a developer for AI applications, transitioning from rapid prototypes to production-grade agentic workflows — including multi-agent systems and MCP servers — that drive measurable ROI.
Architect and code the connective tissue between AI products and customers' live infrastructure, including APIs, legacy data silos, and security perimeters, as part of a collaborative expert team.
Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, and latency.
Identify repeatable field patterns and friction points in the AI stack, converting them into reusable modules or formal product feature requests for engineering teams.
Co-build with customer engineering teams to instill development best practices, ensuring long-term project success and high end-user adoption.
Architect and code the connective tissue between AI products and customers' live infrastructure, including APIs, legacy data silos, and security perimeters, as part of a collaborative expert team.
Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, and latency.
Identify repeatable field patterns and friction points in the AI stack, converting them into reusable modules or formal product feature requests for engineering teams.
Co-build with customer engineering teams to instill development best practices, ensuring long-term project success and high end-user adoption.
- Master's degree or PhD in AI, Computer Science, Science, Technology, Engineering, Mathematics, or a related technical field.
- Experience in Python and relevant machine learning packages (e.g., Keras, PyTorch, HF Transformers).
- Experience in applied AI, with a focus on building systems around pretrained models (e.g., prompt engineering, fine-tuning, RAG, orchestrating model interactions with external tools to deliver solutions).
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google's ADK) and patterns like ReAct, self-reflection, and hierarchical delegation.
- Experience architecting, deploying, or managing solutions on a cloud platform (e.g., Google Cloud Platform).
- Knowledge of LLM-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.