• Ship end-to-end product increments (spec → build → release → operate) • Use the right tool for the job; our current default is a modern TypeScript + React full-stack (server rendering, RSC-style patterns, streaming), and we stay flexible as we learn • Build AI features with disciplined patterns: tool calling, structured outputs, grounding, and streaming UI • Partner with product, design, and SMEs to define outcomes, validate assumptions, and iterate quickly • Turn lessons from shipped features into reusable "paved road" primitives (eval harnesses, guardrails, shared tool/prompt patterns) • Build evaluation + release loops (tests, golden datasets, regressions, targeted human review; LLM grading where it fits) • Own reliability, performance, and cost; instrument with OpenTelemetry and define SLOs for key flows • Enforce security and privacy by design (safe tool access, authZ, auditability, prompt-injection mitigations, PII handling)

• Administer and maintain the Atlan Data Catalog, including user onboarding, role/permission configuration, data asset management, and integration with source systems. • Design, Implement, and continuously evolve Kapitus’s Atlan metamodel (custom asset types, custom properties, relationship models, classification taxonomy, business glossary structure, and propagation/lineage rules). • Partner with Data Engineering, Analytics, Product, and business data stewards to ingest and curate technical and business metadata, ensuring accuracy, completeness, and alignment with governance standards. • Implement data quality monitoring frameworks, configure DQ rules, manage workflows, and coordinate with teams to track and resolve data quality issues. • Build dashboards (e.g. in Tableau, Power BI, or Looker) to report data quality KPI’s, governance effectiveness, and catalog adoption metrics. • Develop repeatable processes, playbooks, and automation scripts for catalog administration, metadata ingestion, and DQ monitoring. • Enable AI/ML initiatives by ensuring trustworthy, well-documented, and governed training data, feature stores, and model lineage. • Support the development of data governance policies, definitions, and stewardship workflows by providing tooling expertise and operational insight. • Contribute to building the data governance operating model and maturing Kapitus’s overall data management capabilities.

• Design and build the internal foundation for AI and data pipelines • Lead the evolution of our IaC (Terraform) strategy • Architect and maintain high-speed delivery pipelines • Support infrastructure for extraction services, image detection and PDF processing • Own the observability-as-a-service layer • Partner with security leads for robust AWS Cognito identity management

• Drive enterprise-wide technical decisions and deliver organizational transformation through AI-empowerment. • Mentor and develop talent for a team of senior engineers, providing technical leadership. • Design and build AI-enhanced capabilities for platform engineering workflows including intelligent monitoring, automated incident response, and predictive capacity planning. • Establish architecture patterns and best practices for integrating AI into platform services, ensuring solutions are maintainable, observable, and fault-tolerant. • Ship AI solutions that measurably improve operational metrics including MTTR reduction, toil automation, incident prevention, and engineering productivity improvement. • Own end-to-end delivery from problem identification through production deployment, training, and knowledge-transfer. • Work in tandem with our product teams and leaders to identify and implement optimal AI solutions throughout our Platform Engineering and Technical Services groups within the company. • Partner with platform engineering, SRE, and IT teams to identify opportunities where AI can transform their operational capabilities and deliver hands-on implementation improvements. • Design and lead the development of high-quality, secure, and observable solutions. • Create frameworks, libraries, and patterns that accelerate AI adoption across the organization and enable teams to extend solutions independently. • Manage the team in achieving ambitious goals, providing regular feedback, and driving continuous improvement. • Participate in all aspects of our software development lifecycle for AI solutions, including planning, defining requirements, developing, and testing those solutions with the team.