Head of Data Infrastructure & Security - Humyn Labs

About Humyn Labs :

Humyn Labs builds the intelligence layer for physical-world AI - systems that perceive, reason, and act in real environments. Our work sits at the intersection of egocentric video understanding, embodied AI, robotics perception, and voice-driven interaction. We move fast, obsess over data quality, and ship at scale.

Humyn Labs converts human action - across sound, sight, movement, and touch - into high-quality multi-modal data signals for physical AI. Operating across 20+ countries in India, southeast Asia, Latin America, and the Middle East : the real-world environments where physical AI deploys, not the labs where it is built.

Our data isn't just collected; it's evaluated, defended, and production-ready. Because before AI can be trusted, its training data must be.

Role Overview :

Humyn Labs is looking for a Head of Data Infrastructure & Security to build and lead the systems that ingest, store, process, and secure our multi-modal data - at scale. This role sits at the intersection of data infrastructure and security : you'll own how large volumes of video, image, and other unstructured data move from the ground into the cloud, how they're stored and served efficiently, how they're processed and labelled for AI use, and how all of it stays protected from breaches, leaks, and unauthorized access.

This is a hands-on leadership role for someone who has genuinely built AI-native data pipelines at scale - not just designed them on paper - and understands the full stack from compute (CPU/GPU) to storage to network.

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 multi-modal data.

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.

Required Qualifications :

- 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 (design, troubleshooting).

- 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, S3 lifecycle & storage tiering

- Video Processing & Transcoding - FFmpeg, AWS MediaConvert/Elemental, video 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 high-growth environment.