Secure data infrastructure for AI

Nitcservices; Berlin; Hybrid; EUR 120.000 - 180.000; Vollzeit

Zusammenfassung

Inceptive, Berlin-based, is seeking a Senior AI Data Security Engineer to architect and own secure data infrastructure across multi-cloud environments, protecting model weights, training data and experiment results. You will collaborate with ML researchers and data engineers to embed security into CI/CD and MLOps, implementing RBAC/ABAC, encryption, audit logging, and threat modeling.

This hands-on role requires an antedisciplinary mindset and a passion for securing AI foundations to enable

Qualifikationen

• 5+ years of experience in architecting, implementing, and operating secure data infrastructure, preferably in AI/ML contexts.
• Proven hands-on expertise with cloud security across multi-cloud environments.
• Deep understanding of data governance (RBAC/ABAC), encryption, audit logging, and data lifecycle management.
• Experience embedding security in CI/CD pipelines and MLOps workflows (containers/Kubernetes).
• Proficiency in threat modeling, secure design reviews, and vulnerability management.

Aufgaben

• Architect, implement, and own secure data infrastructure supporting AI training and deployment pipelines.
• Design and operate authentication systems, access brokers, secrets management, KMS, and egress/ingress controls in a multi-cloud setup.
• Develop and enforce data governance frameworks with RBAC/ABAC, audit logging, encryption, and data lifecycle protocols.
• Embed security controls into MLOps pipelines, including CI/CD security, container and Kubernetes security, namespace isolation, and pod security standards.
• Conduct threat modeling and secure design reviews for existing and new systems across the AI stack.
• Identify, prioritize, and drive remediation for security vulnerabilities across data systems, cloud environments, and ML tooling.
• Build advanced detection and alerting pipelines for anomalous data access and potential data exfiltration.
• Promote security best practices across the organization and educate teams on secure coding and infrastructure principles.
• Embody an antedisciplinary mindset, learning from diverse fields to achieve mission objectives.

Kenntnisse

Secure data infra

Cloud security

MLOps security

Threat modeling

Antedisciplinary mindset

Jobbeschreibung

Development Posted September 23, 2026 at 2:55 AM

Secure data infrastructure for AI

Company

Inceptive

Location

Berlin

Type

Full-time

Commensurate with experience

Who are we looking for?

• Significant experience in secure data infrastructure
• Expertise in cloud security and data governance
• Hands-on MLOps/CI/CD security
• Threat modeling and vulnerability management
• Antedisciplinary mindset

Senior AI Data Security Engineer

Company: InceptiveCategory: Development

Professional Overview

At Inceptive, you'll join a mission-driven, antedisciplinary team dedicated to solving monumental challenges in biology through cutting-edge AI. Your work will directly contribute to developing biological software with the potential to impact billions globally. As we scale our ambitious efforts, the integrity, security, and reliability of our rich, high-quality biological datasets and the infrastructure supporting them are paramount.

This is a critical, senior, and hands-on role where you will be instrumental in architecting, implementing, and owning the secure systems that protect our most sensitive assets, including model configurations, weights, training data, and experimental results. You will not merely advise but actively design and build robust defenses against external adversaries and insider threats across our multi-cloud environment.

You will collaborate closely with ML researchers, data engineers, and computational biologists to secure the entire data lifecycle, from ingestion and training to inference, analysis, logging, and model serving. If you are passionate about securing AI at its foundation and thrive in an environment where your expertise directly enables groundbreaking scientific discovery, Inceptive offers an unparalleled opportunity for impact.

Key Responsibilities

• Architect, implement, and own secure data infrastructure supporting Inceptive's AI model training and deployment pipelines, covering the full spectrum from raw data ingestion to model weight storage and access.
• Design and operate foundational security services, including authentication systems, access brokers, secrets management, key management platforms, and egress/ingress controls within a multi-cloud environment.
• Develop and enforce robust data governance frameworks, incorporating RBAC/ABAC policies, comprehensive audit logging, encryption (at rest and in transit), workload identity management, and data lifecycle protocols.
• Embed stringent security controls directly into our MLOps pipeline, encompassing CI/CD security, container and Kubernetes security, namespace isolation, and pod security standards.
• Conduct proactive threat modeling and secure design reviews for both existing and nascent systems, meticulously identifying attack surfaces across the entire AI technology stack.
• Identify, prioritize, and drive remediation efforts for vulnerabilities across data systems, cloud environments, and ML tooling, specifically addressing AI-centric risks such as data poisoning, model extraction, and unauthorized model access.
• Build advanced detection and alerting pipelines to identify anomalous data access patterns and potential data exfiltration events.
• Establish and champion security best practices across the organization, educating team members on secure coding and infrastructure principles.
• Embody an antedisciplinary mindset, actively seeking to learn and integrate knowledge from diverse fields outside your traditional area of expertise.

Eligibility & Requirements

• Significant experience (e.g., 5+ years) in architecting, implementing, and operating secure data infrastructure and security services, preferably within an AI/ML context.
• Proven hands-on expertise with cloud security practices and technologies across multi-cloud environments.
• Deep understanding and practical experience with data governance frameworks, including RBAC/ABAC, encryption, audit logging, and data lifecycle management.
• Demonstrated ability to embed security within CI/CD pipelines and MLOps workflows, including container and Kubernetes security.
• Proficiency in conducting threat modeling, secure design reviews, and vulnerability management.
• Experience with foundational security services such as authentication systems, access brokers, secrets management, and key management platforms.
• Ability to build detection and alerting mechanisms for security incidents and anomalous behavior.
• Strong communication and collaboration skills, with experience working closely with ML researchers, data engineers, and computational biologists.
• A proactive, "antedisciplinary" mindset, eager to learn and apply knowledge across various domains to achieve mission objectives.