• Support and contribute hands-on to multiple ML platform POCs • Work closely with Applied Scientists, ML Engineers, and internal platform teams • Evaluate platform capabilities across: GPU training and experimentation, real-time and batch inference, orchestration, monitoring, and operability, multi-tenancy, isolation, and scalability • Assess integration points with existing in-house tooling • Perform performance and operability analysis • Contribute technical input to: Build vs buy vs extend decisions, target platform stack recommendations, OPEX and CAPEX justification for rollout

• Support and enhance Pondurance’s MDR Data Pipeline • Focus on enhancing the durability and reliability of HashiCorp-based, containerized Vector endpoints and the systems that deploy them • Act as a subject matter expert for GitHub Actions, Terraform, and Nomad–based CI/CD pipeline • Own and maintain self-service deployment mechanisms for platform frontend • Manage system and application configuration using Salt and HashiCorp tooling • Oversee and maintain Docker-based systems • Implement and maintain monitoring, alerting, and dataflow solutions using Datadog • Participate in on-call rotations and root cause analysis (RCA) • Collaborate with cross-functional teams on reliability, scalability, and continuous improvement initiatives • Document processes, procedures, and troubleshooting steps to support knowledge sharing and team efficiency

• Diseñar, desarrollar y evolucionar Odoo como plataforma central de growth, marketing, analytics y ventas. • Eliminar la dependencia de herramientas externas y habilitar una visión completa del funnel end-to-end. • Combinando arquitectura técnica, analítica avanzada y entendimiento profundo del negocio.

• Build the team + roadmap • Stand up a new platform team (hiring, onboarding, team rituals, execution cadence). • Define a clear, prioritized platform roadmap with measurable adoption outcomes. • Deliver high-leverage platform components • Build “paved roads” that make it easy to ship: reusable patterns, templates, libraries, and reference architectures. • Enable self-service across data + ML workflows with strong governance, security, and observability. • Use a mix of open source and vendor products to build a Data and ML Platform. • Run production-grade operations • Establish quality gates (design reviews, testing strategy, release safety) and platform standards. • Own operational excellence: SLIs/SLOs, on-call readiness, incident response, capacity planning, cost/performance tradeoffs. • Partner across the org • Work closely with Data Engineering, ML Engineering, Analytics, Product, and Security to drive adoption and outcomes.