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MinIO

Field Chief Technology Officer (Field CTO)

RemoteIndia only
Published
Role
Engineering Management
Experience
C-Level
Salary not disclosed
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No BS summary

Super senior C-level technical role at a data/AI storage vendor: 10+ yrs in technical leadership, must thrive as a customer-facing evangelist with Fortune-500 audiences and deep hands-on architecture across AI stacks (LLMs, RAG, vector DBs, PyTorch) lake house analytics (Iceberg/Delta/Trino/Spark), plus Kubernetes and S3. Bengaluru, India (remote).

Core skills

LLMsRAG

Required skills

Milvus/Qdrant/PineconePyTorchTensorFlowRayHugging FaceApache Iceberg/Apache Hudi/Delta LakeTrinoPrestoApache SparkApache FlinkStarRocksKubernetescontainerizationAWS S3 APIREST APIsdistributed systems

What you'll do

  • Act as a trusted technical advisor to C-level executives at Fortune 500 companies.
  • Articulate the strategic value of high-performance object storage in the context of AI readiness and data democratization.
  • Design and validate complex, large-scale architectures for enterprise customers.
  • Guide customers in integrating MinIO seamlessly into their AI/ML pipelines and modern data lakehouse ecosystems.
  • Represent MinIO at major industry conferences, webinars, and meetups.
  • Write technical whitepapers, reference architectures, and blog posts on object storage, generative AI, and modern analytics.
  • Partner closely with enterprise sales and customer success teams on high-stakes deals.
  • Unblock technical objections and provide strategic guidance during Proof of Concepts (PoCs) and large-scale deployments.
  • Serve as the 'voice of the customer' to MinIO's product and engineering teams.
  • Distill market trends, customer pain points, and emerging AI/Analytics patterns into actionable product requirements.

What they require

  • 10+ years of experience in technical leadership roles (e.g., Enterprise Architect, Chief Architect, VP of Engineering, or previous Field CTO).
  • Demonstrated experience in a customer-facing or evangelist capacity, successfully presenting to both highly technical audiences and C-level business executives.
  • Deep architectural understanding of Generative AI and Large Language Models (LLMs), including Retrieval-Augmented Generation (RAG) workflows and vector databases (e.g., Milvus, Qdrant, Pinecone).
  • Strong knowledge of AI/ML data pipelines and frameworks (PyTorch, TensorFlow, Ray, Hugging Face).
  • Understanding of hardware bottlenecks in AI, specifically the challenge of feeding data to GPUs (GPU data starvation) and how high-throughput storage solves this.
  • Expertise in Open Table Formats (Apache Iceberg, Apache Hudi, Delta Lake) and their integration with object storage.
  • Proficiency with modern query engines and compute frameworks (Trino, Presto, Apache Spark, Flink, StarRocks).
  • Solid grasp of the evolution from traditional data warehouses to modern Data Lakehouse architectures.
  • Strong command of Kubernetes, containerization, and cloud-native orchestration.
  • Deep understanding of the AWS S3 API, RESTful APIs, and distributed systems design.

cloud storage server compatible with Amazon S3

AI InfrastructureStartupminio.io/
Salary not disclosed