• The work involves designing and implementing AI solutions aligned with business needs and technological standards. • It includes applying techniques like Retrieval-Augmented Generation (RAG) to improve the precision and relevance of AI interactions. • Developing Agentic RAGs, which incorporate autonomous agents to refine data retrieval and content generation. • Developing AI agents and agent networks, capable of solving complex problems and interacting with each other. • Using tools such as LangChain to create modular architectures for agents. • Designing and managing data ingestion pipelines, implementing serverless architectures and Pub/Sub models to scale data intake. • Using DevOps and MLOps practices for automating the deployment and management of AI models. • Integrating APIs to connect AI models with external systems and to register events and metadata. • Performance optimization includes analyzing system performance, detecting bottlenecks, and implementing caching strategies to improve response times. • Emphasizing collaboration with designers and product teams to ensure AI solutions meet real business and user needs, while maintaining innovation through continuous research and experimentation with new AI technologies.

• Operate within project-specific frameworks while collaborating with cross-functional team members, including product managers, UX designers, and front-end developers • Recommend and implement innovative solutions to drive continuous improvement • Develop and implement new features, as well as enhance and optimize existing functionalities • Partner with product owners to translate strategic objectives into actionable backlog items • Engage with end users to address feature requests and resolve reported issues • Formulate and enforce rigorous testing methodologies

• Build and maintain the core back-end systems that power the Hub • Design and develop secure, scalable services and REST/GraphQL APIs • Implement notification logic, identification flows, and brand integrations • Build and maintain data pipelines (Aurora Postgres, ClickHouse, ETL flows) • Deploy and maintain infrastructure on AWS (ECS, Lambda, S3, CloudWatch, RDS) • Implement MCP (Model Context Protocol) servers and clients for the Hub’s agent-readiness layer • Ensure test coverage, monitoring, and documentation • Contribute to CI/CD pipelines and automation tooling

• Design and develop infrastructure APIs in Go for cloud resource management. • Implement REST and gRPC endpoints across the cloud platform API surface. • Contribute to backend service architecture: API design, data models, service communication. • Integrate services with the platform ecosystem: IAM, Terraform provider, OpenStack, Kubernetes. • Maintain and improve CI/CD pipelines and release automation. • Own services end-to-end: from development through production monitoring and incident response.