• Own the end-to-end delivery of production-ready LLM services • Turn complex data into fast, reliable, and grounded recommendations at scale • Build and operate the core AI systems that power our platform with production-grade performance

• Assess business unit workflows and identify where automation, agents, or custom tooling create real leverage • Design and build agents for internal operations: orchestration, tool-calling patterns, escalation paths, and human-in-the-loop gates • Build custom internal tools, integrations, and infrastructure that support business operations across functions • Evaluate enterprise data systems for AI readiness: quality, accessibility, governance, and latency. Build the access patterns that make enterprise data usable by AI systems. • Define the AI application lifecycle for internally-built systems: how artifacts get verified, tested, deployed, and maintained. Establish quality gates between prototype and production. • Define and enforce AI governance practices: agent permissions, escalation paths, decision audit trails, behavioral monitoring, and the boundary between autonomous action and human oversight • Build and operate internal AI infrastructure: self-service capabilities, deployment guardrails, observability, and cost management • Build tools and interfaces that make AI capabilities accessible to non-technical teams, translating business problems into working systems they can use directly

• Implement new AI features in products • Build tools & frameworks augmenting the capabilities of LLM/VLMs • Build AI automations & AI agents • Fine-tune AI models • Fine-tune and maintain models with the HuggingFace toolkit • Monitor model's performance • Work with and guide internal teams on building custom datasets • Rapidly build and test AI-driven concepts — moving from proof of concept → MVP → production. • Evaluate existing solutions (both internal & external) and conduct experiments on potential improvements

• Lead and execute the vision, strategy, and roadmap for Reddit’s large-scale GenAI Platform. • Define the platform architecture and operating model that enable teams to build, deploy, and scale GenAI products reliably. • Drive the strategy for a unified LAG Gateway supporting internally and externally hosted LLMs through consistent APIs and abstractions. • Set the direction for core platform capabilities such as rate and token limit management, intelligent failover, and production resilience. • Shape Reddit’s approach to an enterprise-grade RAG system • Establish the strategic direction for agentic AI workflows and tool-use patterns across the platform. • Own the end-to-end platform strategy from concept through production adoption and long-term evolution. • Drive MLOps and LLMOps standards across CI/CD, testing, versioning, evaluation, and lifecycle management. • Define best practices for observability, monitoring, governance, and operational excellence across GenAI systems. • Partner across engineering, product, and leadership to align platform investments with company priorities and user needs. • Champion platform thinking with a strong focus on scalability, reliability, performance, and developer experience. • Influence technical direction across teams by turning emerging AI capabilities into a scalable platform strategy.