AI Security Architect

Join the team leading the next evolution of virtual care. At Teladoc Health, you are empowered to bring your true self to work while helping millions of people live their healthiest lives. Here you will be part of a high-performance culture where colleagues embrace challenges, drive transformative solutions, and create opportunities for growth. Together, we’re transforming how better health happens. Summary of Position The Principal AI Security Engineer is a senior technical leader on the AI Security team, responsible for designing, building, and operating security controls for generative AI and Machine Learning (ML) systems across their full lifecycle: data, training, deployment, and runtime. This role is deeply hands-on: you will work directly with data science, MLOps, platform, devops and application teams to secure LLMs, RAG systems, AI agents, and AI-enabled products. You will also lead the intake and review process for AI use cases, helping the organization adopt AI safely and at scale in a highly regulated environment. The ideal candidate combines: · 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 Essential Duties and Responsibilities Secure AI / ML platforms and workloads · 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. AI use case intake & governance · 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. Monitoring, detection & assurance · 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. Strategy, leadership & enablement · 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 ... (truncated, view full listing at source)