• Take part in development and optimization of a large neural network-based tabular model implemented in Python • Profile training and inference pipelines to identify performance bottlenecks • Rewrite critical components in C++ (via PyBind11 or custom extensions) where Python limits us • Improve memory efficiency, latency, and throughput across model pipelines • Ensure correctness, numerical stability, and reproducibility as the model evolves • Collaborate with ML researchers on productionizing new capabilities • Maintain clean abstractions, comprehensive tests, and clear documentation • Shape architectural decisions for our ML systems handling tabular data

• Build and deploy workflow automations (n8n, MS Copilot agents) that solve real problems in Sales, RevOps, and Support • Stand up data pipelines and integrations across our stack (Salesforce, Freshdesk, MS365 Cpilot) • Get hands-on with LLM APIs - prompt engineering, RAG basics, evaluation frameworks • Rapidly prototype and test working solutions (LLM agents, workflow automations, microapps, embedded tools, etc.) • Own our simulation product — voice AI, LLM orchestration, context management • Launch AI features into existing BARBRI products — identify high-value integration points and ship them • Enable non-technical teams to build their own AI agents and automations — act as the expert they can lean on • Architect system integrations across our tooling ecosystem • As the team grows, provide technical direction — own architecture decisions, mentor new engineers, manage vendors • Build RAG pipelines, multi-agent orchestration, and real-world task integration. • Support and improve existing Knowledge Retrieval (search, recommendations, course assistant) projects

• Own video AI from concept to production. • Shape how we deploy and optimize self-hosted LLMs. • Influence architecture and engineering standards. • Directly impact enterprise customer experiences. • Partner with Product to define a roadmap and execute. • Design and build end-to-end video analysis pipelines (visual + audio + text). • Extract sentiment, intent, and semantic meaning from multimodal data. • Deploy models into secure, production environments. • Ensure what you ship works in production. • Own and optimize self-hosted LLM infrastructure. • Manage inference pipelines and performance trade-offs. • Ensure scalability, reliability, and security of models. • Mentor mid-level and junior engineers. • Review code and influence architecture. • Bring clarity to technical discussions. • Identify gaps and opportunities in existing video capabilities. • Define roadmap with Product and deliver meaningful improvements.

• Design and implement AI-powered features integrated into production environments • Integrate LLMs, AI services, and automation layers into backend and frontend systems • Use **Claude Code, CodeX, or similar AI copilots** as part of your engineering workflow • Build guardrails, monitoring, and fallback mechanisms for AI reliability • Optimize prompt architecture, model usage, and system performance • Design and build scalable backend services and APIs • Develop and maintain frontend features and user-facing tools • Architect clean service layers connecting AI components with application logic • Ensure performance, security, and long-term maintainability across the stack • Own features from initial architecture through deployment and iteration • Collaborate with product and design to translate requirements into robust systems • Identify bottlenecks and propose automation or architectural improvements • Contribute to long-term infrastructure and scalability planning • Write clean, maintainable, well-tested code • Contribute to CI/CD pipelines and deployment workflows • Improve observability, monitoring, and system reliability • Maintain high technical standards while moving quickly