• Design and implement the next generation of GenAI-powered business solutions. • Collaborate closely with key stakeholders to translate both business and technical requirements into actionable solutions. • Develop diagrams, models, and documentation to support design, integration, and delivery. • Work with senior architects on complex system integrations.

• Design, develop, and deploy AI-driven applications and services, with a strong focus on Machine Learning and Generative AI use cases. • Build and integrate LLM-based solutions using frameworks such as LangChain, ensuring reliability, scalability, and performance. • Collaborate with product managers, designers, and data teams to translate business requirements into technical AI solutions. • Implement data pipelines, model training workflows, and inference services across different environments. • Optimize models and systems for performance, cost, and scalability in production. • Ensure best practices related to model evaluation, monitoring, versioning, and responsible AI usage. • Contribute to technical discussions, architectural decisions, and proof-of-concepts for AI initiatives. • Stay up to date with emerging AI technologies, tools, and industry trends.

• You will design and implement scalable APIs/services for developing AI agents, including orchestration, memory, tool use, and learner interaction • You will implement rigorous evaluation frameworks to improve agent performance (offline and in-product), and build the instrumentation to make results actionable improvement cycles • You will build data flows/pipelines that allow real-time personalization and learning insights • You will ensure data quality and accessibility for training and evaluating learning agents • You will improve developer velocity with solid API design, testing practices, and realistic architecture choices • You will ship end-to-end product slices: implement backend capabilities and the corresponding lightweight web UI (and occasionally mobile) to unblock product progress • You will build lightweight internal tools/admin surfaces for evaluation, debugging, content/ops workflows, and product iteration • You will collaborate with product/design to deliver high-quality user experiences while keeping scope and maintainability sane • You will help establish the basics of production readiness: monitoring, dashboards, logging/tracing, and performance/cost hygiene • You will contribute to automated testing and deployment pipelines for rapid, safe iteration (CI/CD, rollbacks, environment hygiene) • You will participate in incident response when needed and help shape runbooks, postmortems, and an on-call model appropriate for our current stage

• You will design and implement scalable APIs for agent orchestration and learner interaction • You will build data pipelines that feed agent evaluation and continuous improvement cycles • You will create event streaming infrastructure for real-time learner interaction analysis • You will ensure data quality and accessibility for training and evaluating learning agents • You will improve data infrastructure for cost and performance in close coordination with Data and ML teams • Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles • Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems • Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows) • Participate in on-call rotation and incident response for learning platform reliability • Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model