Senior Technical Consultant - AI Security
New
A
AHEADAI security
Listing location: India; Workplace type: Remote; Structured job location: IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 7+ 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 7+ years of experience in security engineering, AI engineering, platform engineering, forward deployed engineering, solutions consulting, or a related technical role.
- Demonstrate progression to a principal or senior consulting level.
- Bring 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, such as AWS, Azure, or GCP.
- Have 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 in implementation-focused environments.
- 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, as well as foundational cloud, identity, and endpoint security.
- Have scripting and automation experience using 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
- Deploy and operationalize 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-enabled security tools.
- Build cloud-native data ingestion, normalization, and enrichment pipelines for AI telemetry, model activity logs, and prompt/response data.
- Integrate APIs, webhooks, message queues, and automation workflows across AI operations, identity, cloud, and application environments.
- Implement AI security controls and guardrails, including prompt injection defense, model access controls, data leakage prevention, and agentic workflow monitoring.
- Secure non-human identities, including service accounts, API keys, tokens, and autonomous agent credentials.
- Implement red teaming, adversarial testing, model risk scoring, anomaly detection, and AI security monitoring and response.
- Troubleshoot deployment and integration issues and improve reliability through documentation, testing, monitoring, and standardized engineering practices.
- Collaborate with cross-functional teams, mentor engineers and consultants, and provide field feedback on platform and product direction.
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