Forward Deployed Engineer, SLED, Google Public Sector

Minimum qualifications:

• Bachelor's degree in Computer Science, Engineering, a related field, or equivalent practical experience.
• 8 years of experience building and shipping production-grade AI-driven solutions to external or internal customers using Python, TypeScript or comparable languages.
• Experience building scalable pipelines for structured, unstructured data, incorporating vector databases and RAG-like architectures to power enterprise-grade AI solutions.
• Experience architecting scalable AI systems on cloud platforms.
• Experience leading technical discovery sessions with executive stakeholders (C-suite) and engineering teams to define AI and hardware infrastructure requirements.

Preferred qualifications:

• Master’s degree or PhD in AI, Computer Science, or a related technical field.
• Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google’s ADK) and complex patterns like ReAct, self-reflection, and hierarchical delegation.
• Proven experience architecting integrated systems, navigating real-time inference constraints, and implementing model quantization for resource-constrained environments.
• Proficiency in Vertex AI Pipelines, Kubeflow, or MLflow to implement CI/CD/CT automation and experimentation.
• Knowledge of "LLM-native" metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
• Designing resilient data engineering pipelines using BigQuery and VertexAI for enterprise-scale analytics.

Responsibilities

• Serve as a developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable ROI.
• Architect and code the "connective tissue" between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters as part of an 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 Google’s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.
• Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.