- Architect and scale orchestration engines for complex agentic workflows, with low-latency tool execution and robust state management
- Design high-performance RAG infrastructure, including vector database integration, search indexing, query processing, result ranking, semantic caching, and automated metadata extraction
- Develop automated infrastructure for large-scale golden set simulations, error analysis pipelines, and hillclimbing experiments
- Collaborate with the modeling team to productionize LLM capabilities as hardened, multi-tenant microservices with guardrails and observability
- Direct infrastructure strategy for model routing, prompt caching, and token optimization
- Build backend infrastructure powering Snowflake Intelligence, Cortex Agents, and Cortex Search
- Perform cross-layer debugging across services using logs and telemetry
- Evaluate customer problems and determine whether they represent bespoke issues or platform gaps worth addressing
Requirements
- Bachelor’s degree in Computer Science or a related technical field
- 7+ years of experience building distributed systems, high-throughput APIs, or backend infrastructure for AI/ML products
- Deep proficiency in Go or Java and Python
- Strong understanding of database internals, distributed state management, and cloud-native architecture
- Experience with Kubernetes and FoundationDB or similar technologies
- Familiarity with vector indices, agent platforms, and scalable data pipelines
- Experience in a customer-facing technical role
- Product judgment to distinguish bespoke customer problems from platform gaps
- Cross-layer debugging using logs and telemetry
- Experience with eval frameworks for LLM/agent systems, including defining quality metrics and improving quality systematically
- Comfort with ambiguity on open-ended, externally-driven problems
- Bonus experience with query optimization, SQL engine internals, multi-tenant systems handling sensitive enterprise data, and large-scale search infrastructure
Core Competencies
Demonstrates expertise in architecting and scaling orchestration engines and backend infrastructure for AI/ML products, with a strong focus on distributed systems, cloud-native architecture, and cross-layer debugging. Proficient in developing high-performance RAG infrastructure and automated simulations while collaborating effectively with technical teams.
Highest-signal resume keywords
- Distributed Systems Development
- Go, Java, And Python Proficiency
- Kubernetes And FoundationDB Experience
- Cross-Layer Debugging Using Logs And Telemetry
- LLM/Agent Systems Evaluation Frameworks
ATS Optimization Keywords
Hard Skills
- Distributed Systems
- High-Throughput APIs
- Backend Infrastructure
- Database Internals
- Cloud-Native Architecture
- Query Optimization
- SQL Engine Internals
- Error Analysis Pipelines
- Automated Metadata Extraction
- Scalable Data Pipelines
Soft Skills
- Product Judgment
- Comfort With Ambiguity
- Customer-Facing Technical Role
Certifications & Qualifications
- Bachelor’s Degree In Computer Science
Industry Keywords
- AI/ML Products
- Agent Platforms
- Multi-Tenant Systems
- Large-Scale Search Infrastructure
Tools & Technologies
- Kubernetes
- FoundationDB
- Vector Database
- Telemetry Tools
- Logs Analysis