Head of Data Infrastructure & Security

Zyoin Group; Bengaluru; On-site; INR 9,000,000 - 15,000,000; Full time

Job summary

Zyoin Group invites applications for Head of Data Infrastructure & Security to guide scalable, secure data pipelines for AI-driven robotics and perception systems. Lead data storage, CDN strategy, and secure data flows from edge to cloud in a fast-growing AI company.

You will oversee a team spanning data infrastructure, pipeline engineering, and data security, collaborating with AI/ML, product, and engineering teams while reporting on health, posture, and risk to leadership.

Qualifications

• Proven hands-on experience building AI-native data pipelines at scale.
• Strong understanding of video storage, CDN architecture, and egress optimization.
• Experience architecting large-scale data ingestion from ground/edge to cloud.
• Solid understanding of compute infrastructure (CPU/GPU) and how it interacts with storage and data pipeline design.
• Strong data security background - access control, encryption, breach prevention and response.
• Experience with major cloud platforms (AWS, GCP, or Azure) at scale.
• Ability to operate both strategically (roadmap, architecture decisions) and hands-on
• Strong communication skills - able to translate technical infrastructure and security trade-offs for leadership.

Responsibilities

• Design and own end-to-end pipelines for ingesting, storing, and processing large volumes of multi-modal data (video, image, audio, and other unstructured formats).
• Architect systems for large-scale data upload from ground/edge sources into the cloud, optimizing for reliability, speed, and cost.
• Own video storage architecture, CDN strategy, and egress cost/performance optimization.
• Build and scale data labelling and processing pipelines that feed AI/ML workflows.
• Design infrastructure that balances CPU/GPU compute needs with storage architecture for AI workloads.
• Make informed trade-offs on storage tiers, compute allocation, and cost as data volume and processing needs grow.
• Ensure infrastructure scales smoothly as data volume, model complexity, and team usage increase.
• Own the security of data across its lifecycle - in transit and at rest; enforce access controls and encryption standards.

Skills

Data pipelines

Cloud infrastructure

Data security

Video storage

CDN architecture

Cross-functional leadership

Security governance

MLOps

Communication to leadership

Edge-to-cloud ingestion

Tools

AWS

GCP

Azure

Job description

We are hiring for a Head of Data Infrastructure & Security role with a fast-growing AI company building the intelligence layer for physical-world AI. The company is working at the intersection of robotics perception, embodied AI, egocentric video understanding, and voice-driven interaction to develop AI systems that can perceive, reason, and act in real-world environments.

Work on challenging robotics and AI problems involving 3D perception, computer vision, multimodal learning, sensor data, and embodied intelligence. You'll collaborate with a strong research and engineering team to develop systems that operate in complex, real-world environments, work on cutting-edge research, and contribute directly to building the next generation of intelligent physical-world systems.

Roles and Responsibilities:

• Proven, hands-on experience building AI-native data pipelines at scale - not just conceptual/theoretical exposure.
• Strong understanding of video storage, CDN architecture, and egress optimization.
• Experience with data labelling and processing pipelines for large datasets.
• Experience architecting large-scale data ingestion from ground/edge to cloud.
• Solid understanding of compute infrastructure (CPU/GPU) and how it interacts with storage and data pipeline design.
• Strong data security background - access control, encryption, breach prevention and response.
• Experience with major cloud platforms (AWS, GCP, or Azure) at scale.
• Ability to operate both strategically (roadmap, architecture decisions) and hands-on
• Strong communication skills - able to translate technical infrastructure and security trade-offs for leadership.
• Cloud Storage & CDN - AWS S3/GCS/Azure Blob CloudFront/Cloudflare/Akamai/Fastly, pipeline orchestration at scale.

Nice to Have

• Experience in a company processing large-scale video or multi-modal AI training data.
• Experience with MLOps or data pipeline tooling for AI/ML workflows.
• Relevant security certifications (CISSP, OSCP, or equivalent).
• Experience building or scaling a data infrastructure/security function in a startup or

Key Responsibilities

Multi-Modal Data Pipeline Architecture

• Design and own end-to-end pipelines for ingesting, storing, and processing large volumes of multi-modal data (video, image, audio, and other unstructured formats).
• Architect systems for large-scale data upload from ground/edge sources into the cloud, optimizing for reliability, speed, and cost.
• Own video storage architecture, CDN strategy, and egress cost/performance optimization.
• Build and scale data labelling and processing pipelines that feed AI/ML workflows.

Compute & Storage Infrastructure

• Design infrastructure that balances CPU/GPU compute needs with storage architecture for AI workloads.
• Make informed trade-offs on storage tiers, compute allocation, and cost as data volume and processing needs grow.
• Ensure infrastructure scales smoothly as data volume, model complexity, and team usage increase.

Data Security

• Own the security of data across its lifecycle - in transit (ground-to-cloud upload, egress) and at rest (storage, labelling pipelines).
• Design and enforce access controls, encryption standards, and monitoring to protect data from breaches, leaks, and unauthorized access.
• Build incident response processes specific to data security risks and lead response when issues arise.
• Establish security and compliance standards appropriate for large-scale, sensitive

Leadership

• Build and lead a team spanning data infrastructure, pipeline engineering, and data security as the function scales.
• Partner closely with AI/ML, product, and engineering teams to ensure data pipelines meet their evolving needs.
• Report to leadership on infrastructure health, data security posture, and risk in clear actionable terms.
• Balance engineering velocity with security rigor and cost efficiency.