Troveo AI

• Develop and clearly articulate the product strategy, aligning with company objectives and the evolving market landscape. • Own products and outcomes end to end, from initial concept through deployment and continuous enhancement, ensuring delivery on time, within scope, and beyond expectations. • Bridge complex technical solutions and tangible business value, making informed prioritization decisions and smart trade-offs. • Design and continuously optimize the platforms through which licensors provide and transfer large-scale data to Troveo, from multimodal content to enterprise workflow data. • Own the end-to-end ingestion experience, from onboarding workflows to file-transfer reliability, making it seamless for partners of all technical sophistication levels. • Partner closely with engineers to build and refine a cost-effective, high-throughput data pipeline covering profiling, deduplication, transcoding, annotation, and downstream delivery. • Help design processing pipelines and logic tailored to diverse data types, including video files, structured metadata, and other custom formats submitted by licensors. • Identify bottlenecks, propose improvements, and drive execution across pipeline stages. • Own analytics and data-driven feature improvement, using metrics to identify opportunities, prioritize work, and measure impact. • Dive deep into the data to address operational data issues, maintaining data quality and consistency across the pipeline. • Partner across teams to meet client delivery needs, including data reconciliation across systems.

• Design, develop, and maintain scalable backend services and APIs using microservices architecture • Build and operate production workloads on Kubernetes (deployments, services, ingress, autoscaling, Helm, etc.) • Work extensively with Elasticsearch - indexing, querying, aggregations, performance tuning, and cluster management • Apply distributed systems concepts to design fault-tolerant, highly available, and scalable systems • Optimize application performance and reliability in distributed and containerized environments • Design and implement robust CI/CD pipelines with GitOps and Kubernetes-native deployments • Troubleshoot complex issues across services, Kubernetes clusters, and Elasticsearch • Improve system observability, monitoring, and alerting in distributed environments • Participate in architectural decisions and mentor engineers on distributed systems and Kubernetes best practices • Collaborate with platform teams to evolve internal tooling and developer experience