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Teladoc Health · 12 hours ago

Principal AI Cybersecurity Engineer

United States

Full-time

Remote

Senior Level, Lead/Staff

$165K/yr - $185K/yr

7+ years exp

Teladoc Health is a global virtual care leader, offering comprehensive virtual care solutions. They are seeking a Principal AI Cybersecurity Engineer who will design, build, and operate security controls for generative AI and Machine Learning systems, collaborating with various teams to ensure the secure adoption of AI technologies.

Health CareMental HealthmHealthPrimary and Urgent CareTelehealthWellness

No H1B

Responsibilities

Lead security architecture and threat modeling for AI/ML systems, including LLMs, RAG pipelines, agents, and AI-powered applications

Design and implement security controls as code (services, libraries, infrastructure-as-code, policy-as-code) for AI/ML platforms and workloads

Lead and help setup the basic infrastructure needed to safely rollout AI - MCPs, LLMs, pipelines, Test harness for AI (ie: harmbench), intake automation

Partner with data science and MLOps teams to harden:

Data ingestion and labeling

Training and fine-tuning pipelines

Model registries and deployment workflows

Inference APIs, agents, and integrations

Define and champion secure reference architectures and patterns for common AI use cases and focus on composable architecture

Design, implement, and continuously improve the intake, triage, and review process for AI/ML and generative AI use cases across the organization

Build and automate self-service workflows (e.g., request forms, risk questionnaires, routing, approvals) that balance speed of delivery with security, privacy, and compliance with a focus on risk scoring and scorecards

Define risk-based criteria for AI use case approval, including data sensitivity, model and vendor selection, integration patterns, and control requirements; this will involve in re-mapping the complete end to end lifecycle

Review proposed AI solutions from concept through deployment, providing clear, actionable guidance to product and engineering teams

Maintain visibility into the AI use case portfolio and risk posture, and provide regular reporting to leadership and governance bodies

Establish and maintain monitoring and detection for AI-specific threats, such as:

Prompt injection and jailbreak attempts

Data exfiltration and sensitive data exposure

Misuse or abuse of AI tools and agents

Anomalous model or pipeline behavior

Integrate AI/ML systems with existing logging, SIEM, and incident response processes

Lead or participate in AI-focused security assessments, red-teaming, and adversarial testing; drive remediation and verification

Help define and evolve the organization’s AI security strategy, standards, and roadmap in partnership with Security, Engineering, Data, Legal, Privacy, and Risk

Translate global privacy, data sovereignty, and regulatory requirements into practical technical controls for AI workloads across multiple cloud environments

Prepare and deliver executive-ready briefings and narratives on AI security risks, controls, and progress

Mentor other engineers and serve as THE internal subject matter expert on AI/ML security, generative AI, and LLM-based systems

Qualification

AI/ML securityCloud security architectureSecurity controls implementationProgramming PythonProgramming othersAI/ML technologiesThreat modelingData protectionMonitoringDetectionAnalytical skillsCommunication skillsProblem-solving skillsLeadership

Required

7+ years of experience in information security, security engineering, or related fields, including significant time building and securing production systems

3+ years of hands-on experience with AI/ML technologies (such as LLMs, RAG, model training/fine-tuning, MLOps, or AI-powered products), including implementation of security controls or guardrails for these systems

Strong programming skills in one or more relevant languages (e.g., Python, TypeScript/JavaScript, Go, or similar), with a track record of contributing to production-grade tools, services, or libraries

Deep understanding of cloud security architecture and controls on at least one major cloud platform (AWS, Azure, or GCP), including identity, networking, secrets management, data protection, logging, and monitoring

Experience designing and implementing controls in a highly regulated environment; healthcare or financial services preferred

Demonstrated ability to lead complex technical initiatives across multiple teams, from problem definition through design, implementation, and adoption

Proven ability to communicate complex technical and risk topics clearly to both engineering teams and senior leadership

Strong security engineering and cloud architecture experience

Deep, current familiarity with modern AI/LLM tooling and practices

Familiar and can cover basic coding within the AI tooling space (python, others)

The ability to communicate clearly with senior leadership and influence enterprise wide strategy

Preferred

Practical experience securing LLM- and genAI-based systems, such as: RAG architectures backed by internal data, AI assistants, copilots, or agents integrated with enterprise tools, Fine-tuned models and model hosting platforms

Experience with AI IDE tools: cursor, windsurfer, others; Knows the security problems and has practical solutions that balances innovation with innovation

Familiarity with AI/ML frameworks and ecosystems (e.g., TensorFlow, PyTorch, Scikit-learn) and/or modern LLM development stacks and IDEs (e.g., API-based LLMs, self-hosted models, AI-enhanced coding tools)

Experience with: Security for data pipelines, feature stores, and model registries; Detection engineering or SIEM tuning for AI-related events; Red-teaming or adversarial testing of AI systems

Evidence of ongoing engagement with AI and security (such as side projects, open-source contributions, lab environments, publications, or conference talks)

Familiarity with emerging AI security and safety standards and forward-looking industry guidance and horizon reports

Relevant certifications (e.g., cloud security, security engineering, or governance) are a plus

Strong analytical and problem-solving skills, with the ability to operate effectively in a fast-evolving technical and regulatory landscape

High level of integrity and ethical conduct