25 days ago
Remote, South AfricaStaff+
Responsibilities
- Lead customer and partner technical discovery and define scalable AIStor architectures aligned with business, application, data, and infrastructure requirements.
- Design and validate architectures for AI/ML training and inference, analytics, data lakehouse environments, high-performance pipelines, large object workloads, and modern application data services.
- Advise customers on object storage, S3-compatible architectures, distributed systems, metadata, replication, erasure coding, security, performance tuning, and namespace design.
- Lead technical evaluations, configurations, performance testing, interoperability validation, demonstrations, and proof-of-value engagements.
- Guide customers from evaluation through production onboarding and broader adoption across workloads, teams, and use cases.
- Provide technical leadership for Kubernetes, containers, automation, hybrid cloud, private cloud, public cloud, and edge deployments.
- Collaborate with technology partners, resellers, Sales, Product, Engineering, and Customer Engineering teams on integrated solutions.
- Deliver architecture presentations, whiteboard sessions, workshops, demonstrations, and technical enablement.
- Support RFPs, RFIs, RFQs, security reviews, architecture questionnaires, and competitive evaluations.
- Capture customer requirements, competitive insights, workload patterns, and integration needs to inform product and engineering priorities.
Requirements
- 10+ years of experience in a senior customer-facing technical role such as Field Architect, Solutions Architect, Sales Engineer, Systems Engineer, or Technical Consultant.
- Strong background in enterprise or object storage, distributed systems, cloud infrastructure, data platforms, or high-performance data-intensive applications.
- Strong understanding of object storage and S3-compatible architectures, HTTP, JSON, security models, replication, data protection, and scale-out system design.
- Hands-on experience with Linux, networking, storage systems, virtualization, containers, Kubernetes, and modern data center infrastructure.
- Experience designing or supporting AI, machine learning, analytics, data lakehouse, or large-scale data pipeline environments.
- Experience with public and hybrid cloud environments, including AWS, Azure, GCP, OCI, or private cloud platforms.
- Ability to conduct technical discovery, design architectures, build demonstrations, guide proof-of-value execution, analyze performance, and communicate tradeoffs.
- Familiarity with Databricks, Starburst, Spark, Trino, Iceberg, Delta Lake, Kafka, PyTorch, TensorFlow, or other AI and analytics frameworks is preferred.
- Ability to work across compute, storage, networking, security, identity, observability, backup, disaster recovery, and application teams.
- Strong verbal, written, whiteboarding, and presentation skills for technical and executive audiences.
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field, or equivalent practical experience.
- Willingness to travel as needed to support customers, partners, field events, and internal activities.
Benefits
- Travel is required as needed for customers, partners, field events, and internal team activities.
Tech Stack
Categories
Solutions Engineering
