• Be the technical owner of Curative’s F&A platform. • Pull, transform, and reconcile large volumes of financial data. • Collaborate closely with F&A team to identify discrepancies and develop solutions. • Build AI agents to automate support and billing questions. • Manage codebase, architectural decisions, and quality control of a Python + TypeScript application. • Handle sensitive PHI and financial information with data privacy discipline. • Monitor platform performance and propose changes based on operational metrics. • Maintain stakeholder relationships and co-own the product plan with F&A.

• Design, build, and operate orchestration frameworks supporting long-running, stateful AI workflows and agent execution systems • Develop distributed retrieval architectures spanning vector, graph, search, and relational data platforms • Build shared platform services, reusable runtime components, and tool registries supporting AI capabilities across Fortress products • Implement resilient event-driven and change-data-capture (CDC) pipelines with strong consistency, retry, and fault-tolerance guarantees • Develop typed APIs and integration contracts supporting AI workflows and platform interoperability • Improve scalability, performance, reliability, and operational efficiency across orchestration and retrieval infrastructure • Support secure multi-tenant deployments across cloud, hybrid-cloud, and air-gapped environments • Build and maintain observability, telemetry, tracing, centralized logging, governance, and policy enforcement tooling • Contribute to engineering standards and architectural patterns for orchestration, retrieval, and platform infrastructure • Collaborate cross-functionally with platform engineering, product engineering, security, and AI teams to support platform adoption and operational excellence • Participate in troubleshooting, incident response, root cause analysis, and operational support activities related to platform infrastructure • Continuously evaluate emerging technologies, tooling, and engineering practices relevant to AI infrastructure and distributed systems

• Develop and implement data architectures that align with business needs and leverage Snowflake’s capabilities for data warehousing, data lakes, and data engineering. • Design and implement data solutions that address specific business needs, such as BI, ETL, and AI/ML. • Assist with migration of data from legacy systems to Snowflake. • Ensure data accessibility, security, and compliance with relevant regulations and industry standards. • Design and implement data pipelines for ingesting, transforming, and loading data into Snowflake from various sources. • Design and implement data models within Snowflake, considering data relationships, data types, and storage requirements. • Monitor and optimize query performance, data ingestion, and transformation processes within the Snowflake environment. • Collaborate with stakeholders, including business users, data engineers, data scientists, and other IT teams, to understand requirements and deliver solutions. • Evaluate and select appropriate tools and technologies for data integration, transformation, and analytics within the Snowflake ecosystem. • Provide technical leadership and guidance to data engineering and data analytics teams.

Role Description Volta builds and operates large-scale GPU compute infrastructure for AI workloads. Our platform is Kubernetes-native, spans multiple regions, and delivers virtual machines, storage, and networking through a fully automated infrastructure stack built on custom Kubernetes operators. Platform Engineers work at the intersection of infrastructure and software development. You will translate three key inputs into durable platform capabilities: - Product roadmap requirements from the product team - Operational learnings from the bring-up team - Security guidance from the security engineering team What You Will Be Doing: - Design and implement Kubernetes operators and controllers that manage the lifecycle of compute, storage, and networking resources - Work closely with the product team to understand roadmap requirements and implement the platform capabilities that support them - Collaborate with the bring-up team to identify operational pain points and turn them into scalable platform features - Improve and extend the northbound API layer — the interface between user-facing services and the underlying infrastructure platform - Build and extend confidential computing capabilities across the platform stack — from secure bare metal and confidential VMs to Confidential Containers (CoCo) - Integrate security guidance from the security engineering team into platform-level controls and remediate security findings at the platform layer - Build platform capabilities around networking: reliability, performance, and observability of the overlay and underlay network stack - Contribute to storage platform improvements: provisioning workflows, attachment reliability, performance tuning, and failure handling - Own observability as a platform concern — instrument services, define meaningful metrics, and build tooling that gives the team visibility into platform health - Participate in code review, technical design discussions, and cross-team collaboration in an Agile (Kanban or Scrum) environment Qualifications - 3–5 years of software engineering experience, with a meaningful portion spent on infrastructure or platform systems - Working proficiency in at least one relevant language — Python, Go, or Rust — with experience writing production-grade backend services or automation, and a willingness to work across languages as the codebase evolves - Solid understanding of Kubernetes internals: the control loop model, CRDs, controllers/operators, and reliable reconciliation logic - Comfortable working close to the infrastructure layer — Linux, networking fundamentals, and distributed systems behaviour - Experience designing and building APIs or service interfaces that other teams depend on - Strong engineering fundamentals: clean code, testing, version control, code review, and CI/CD practices Requirements - Fluency with AI-assisted development, and interest in scaling agent-assisted workflows across the team (agentic CLI tools, MCP, skills, APIs) to amplify delivery - Familiarity with confidential computing technologies: TEEs, AMD SEV, Intel TDX, or Confidential Containers (CoCo) - Experience integrating security requirements into platform or infrastructure systems - Familiarity with high-performance networking: overlay protocols, BGP, RDMA, or packet-processing frameworks - Hands-on experience with distributed storage systems (Ceph or similar) at an engineering level - Background building Kubernetes operators using frameworks such as Kopf, controller-runtime, or similar - Experience with observability tooling: Prometheus, Grafana, OpenTelemetry, or structured logging in distributed systems - Exposure to GPU infrastructure or HPC environments