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 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. Qualifications Expected for Position - 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. Preferred Qualifications: · 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. This role is a fit if you: · Regularly build, break, or secure AI/ML or LLM-based systems in your day-to-day work or personal projects. · Are comfortable reading and writing code, experimenting with new AI tools, and wiring them into real systems. · Enjoy turning ambiguous AI ideas and risks into concrete architectures, controls, and automation. · Can move fluidly between deep technical discussions and concise, executive-level explanations. This role is not a fit if you: · Prefer to focus solely on policy, governance, or vendor assessments without hands-on technical work. · Do not actively engage with current AI/LLM tooling, research, and emerging practices. · “Describe a specific LLM or AI/ML system you have secured. What were the main risks and what controls did you implement?” · “What AI tools, libraries, or environments do you actively use or experiment with today (work or personal), and for what?” · “What do you see as the most important AI security or safety developments on the horizon over the next few years, and why?” The base salary range for this position is $180,000 - $190,000. In addition to a base salary, this position is eligible for a performance bonus and benefits (subject to eligibility requirements) listed here: Teladoc Health Benefits 2026. Total compensation is based on several factors including, but not limited to, type of position, location, education level, work experience, and certifications. This information is applicable for all full-time positions. We follow a Flexible Vacation Policy, intended for rest, relaxation, and personal time. All time off must be approved by your manager prior to use. You will also receive 80 hours of Paid Sick, Safe, and Caregiver Leave annually. This applies to full-time positions only. If you are applying for a part-time role, your recruiter can provide additional details. As part of our hiring process, we verify identity and credentials, conduct interviews (live or video), and screen for fraud or misrepresentation. Applicants who falsify information will be disqualified. Teladoc Health will not sponsor or transfer employment work visas for this position. Applicants must be currently authorized to work in the United States without the need for visa sponsorship now or in the future. Why join Teladoc Health? - Teladoc Health is transforming how better health happens. Learn how when you join us in pursuit of our impactful mission. - Chart your career path with meaningful opportunities that empower you to grow, lead, and make a difference. - Join a multi-faceted community that celebrates each colleague’s unique perspective and is focused on continually improving, each and every day. - Contribute to an innovative culture where fresh ideas are valued as we increase access to care in new ways. - Enjoy an inclusive benefits program centered around you and your family, with tailored programs that address your unique needs. - Explore candidate resources with tips and tricks from Teladoc Health recruiters and learn more about our company culture by exploring #TeamTeladocHealth on LinkedIn. As an Equal Opportunity Employer, we never have and never will discriminate against any job candidate or employee due to age, race, religion, color, ethnicity, national origin, gender, gender identity/expression, sexual orientation, membership in an employee organization, medical condition, family history, genetic information, veteran status, marital status, parental status, or pregnancy). In our innovative and inclusive workplace, we prohibit discrimination and harassment of any kind. Teladoc Health respects your privacy and is committed to maintaining the confidentiality and security of your personal information. In furtherance of your employment relationship with Teladoc Health, we collect personal information responsibly and in accordance with applicable data privacy laws, including but not limited to, the California Consumer Privacy Act (CCPA). Personal information is defined as: Any information or set of information relating to you, including (a) all information that identifies you or could reasonably be used to identify you, and (b) all information that any applicable law treats as personal information. Teladoc Health’s Notice of Privacy Practices for U.S. Employees’ Personal information is available at this link.

• Develop and implement RAG (Retrieval-Augmented Generation) solutions with a focus on integrating new data sources into our vector databases • Design and optimize APIs to support and scale our systems • Use Microsoft Copilot Studio (formerly Power Virtual Agents) to develop innovative solutions • Connect and process large data volumes while ensuring data protection, security standards, and compliance in cloud environments • Develop and integrate chatbots into multi-channel environments (e.g., Microsoft Teams, web apps) • Automate business processes with Azure Logic Apps, Azure Functions, and Power Automate • Design and optimize workflows to automate data flows and tasks • Collaborate with the team to continuously improve our systems and processes • Use Git for version control and team collaboration

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Effectual AI Cloud Engineers are members of the Public Sector Program Management team responsible for ensuring that customer-facing projects are delivered with exceptional customer satisfaction and technical excellence. The AI Cloud Engineer designs, deploys, modernizes, and optimizes AI-enabled cloud environments. - Deep expertise in AI, cloud architecture, and AI-driven workloads in AWS environments - Accelerate legacy-to-cloud migrations and advise leadership on AI strategy and vision - Hands-on engineering capability and architectural oversight - Assess current-state environments and implement secure, scalable, compliant AI cloud solutions - Stay current on leading practices to deliver high-quality, well-conceived solutions to customers A Glimpse into the Daily Routine of an AI Cloud Engineer: - Architect and refine AI-driven cloud solutions within AWS - Deploy and optimize workloads through managed AWS AI services and containerized platforms - Modernize legacy applications and accelerate secure cloud-native migrations - Evaluate existing infrastructure to recommend AI-enabled enhancements aligned with mission objectives - Implement Infrastructure as Code and CI/CD automation - Integrate AI capabilities into enterprise systems and data platforms - Leverage tools such as Kiro (formerly Amazon Q Developer) to enhance development efficiency - Participate in architecture and security reviews - Advise technical leadership on AI strategy, roadmap execution, and cloud optimization initiatives Qualifications - Bachelor’s degree in related discipline - AWS Professional Certification(s) and AWS Certified Machine Learning - Specialty - Minimum of 8 years of experience in specialized AI cloud technologies including 4 years specializing in AWS AI cloud technologies - 10 years of on-the-job experience in lieu of Bachelor’s Degree - Must be a US Citizen - Proven experience to architect AI solutions in secure, compliance-driven ecosystems - Deep expertise in AWS AI services including model development, deployment, and integration - Strong experience in cloud-native architecture, automation, and DevSecOps practices - Proven experience modernizing legacy systems and implementing cloud transformation strategies - Experience designing scalable AI pipelines using managed cloud services - Strong understanding of federal security compliance frameworks and cloud governance - Experience performing cloud cost modeling and AI workload financial optimization - Strong problem-solving and troubleshooting skills - Excellent communication and collaboration skills to work with cross-functional teams - Ability to work with multiple clients, in parallel - Ability to work Eastern Standard Time Zone schedule Nice-to-Have Skills and Experience - Active Clearance or Public Trust (DOJ Preferred) - Familiarity with generative AI architectures and large language model deployment - Experience with container orchestration platforms including Kubernetes and EKS - Strong background in DevSecOps practices within regulated government programs - Experience integrating AI into enterprise ERP, data lake, or mission systems - Knowledge of Zero Trust architectures within cloud environments - Experience advising executive leadership on AI adoption strategy and digital transformation initiatives

• Design and deliver an agentic layer and a rich context graph that automates complex work with deep integration into customers’ knowledge repositories. • Drive architecture & delivery of production LLM/GenAI systems: Context engineering, Agentic workflows, Model evaluations, Multi-model strategies • Define and implement evaluation: Offline and online metrics, Gold sets and regression tests, Human review loops, A/B experiments • Operationalize LLMOps: Deployment patterns, Observability and monitoring, Incident response, Performance and cost optimization (latency, throughput, token spend) • Build guardrails & security: Prompt-injection defenses, Data-exfiltration prevention, Tool permissions and action constraints, Safe handling of sensitive data • Establish engineering standards: Reference implementations, Reusable libraries, Review practices, Documentation that accelerates the team • Mentor and influence: Raise the bar on system design and code quality, Align multiple teams on outcomes and timelines, Act as a technical force multiplier