Principal Technical Consultant - AI Security
New
A
AHEADAI security
Listing location: India; Workplace type: RemoteFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 10+ years of experience in security engineering, AI engineering, platform engineering, forward deployed engineering, solutions consulting, or related technical roles
- Required Skills
- AWSPythonBashGCPAzureRESTful APIs
Requirements
- Have 10+ years of experience in security engineering, AI engineering, platform engineering, forward deployed engineering, solutions consulting, or a related technical role.
- Bring demonstrated progression to a principal or senior consulting level.
- Have hands-on experience securing AI systems, LLM applications, or AI production pipelines.
- Have experience deploying cloud-native architectures on at least one major cloud provider: AWS, Azure, or GCP.
- Bring a strong background in data integration, telemetry pipelines, normalization, and security analytics workflows applied to AI systems.
- Have experience working directly with customers, executive stakeholders, or cross-functional delivery teams.
- Have experience developing technical proposals, statements of work, and customer engagement scoping documents.
- Understand AI security risks including prompt injection, model theft, data poisoning, adversarial attacks, and agentic AI risks.
- Have scripting and automation experience with Python, PowerShell, or Bash.
- Be familiar with APIs and integration patterns including REST, webhooks, and event-driven workflows.
- Have working knowledge of LLMs, vector search, retrieval-augmented generation, model evaluation, and AI assistants or agents.
- Hold a bachelor’s degree in computer science, information security, or engineering, or have equivalent practical experience.
Responsibilities
- Lead the deployment and operationalization of AI security solutions in customer and enterprise environments.
- Develop technical proposals, statements of work, solution architectures, and implementation plans for AI security engagements.
- Design and implement integrations across AI platforms, model registries, LLM gateways, vector databases, and AI security tooling.
- Build cloud-native pipelines for AI telemetry, model activity logs, and prompt/response data.
- Integrate APIs, webhooks, message queues, and automation workflows across AI, identity, cloud, and application ecosystems.
- Implement AI security controls and guardrails, including prompt injection defense, model access controls, and data leakage prevention.
- Secure non-human identities, including service accounts, API keys, tokens, and autonomous agent credentials.
- Support red teaming, adversarial testing, model risk scoring, and anomaly detection for AI systems.
- Troubleshoot deployments and improve solution reliability through documentation, testing, monitoring, and standardized engineering practices.
- Advise stakeholders, mentor engineers and consultants, and contribute to pre-sales, product feedback, and architectural standards.
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