Senior Security Engineer

<div class="content-intro"><p><strong>Who is Eleos Health?</strong></p> <p>Today, more people than ever are speaking publicly about their mental health. Whether it's ourselves, our friends and family or even public figures, taking care of your behavioral health is no longer a taboo, it's vital, and it's only human. <br><br>Eleos is on a mission to help deliver the world's most effective behavioral care through data, measurement, and personalization. Or simply put, <strong>we want to give clinicians the support they need to do the important work only they can do</strong>.</p></div><h2><strong>What is this opportunity?</strong></h2> <p>Senior Security Engineer to act as a hands-on technical leader inside our security org, part builder, part architect, part trusted partner to R&D. You'll sit in design reviews, push back (constructively) on architecture choices, prototype the controls we're missing, and help R&D ship faster and safer.</p> <p>This role suits an engineer who thinks in systems, codes when needed, and is energized by securing modern AI-driven, multi-cloud environments.</p> <h2><strong>What you'll do</strong></h2> <ul> <li><strong>Act as a security architect for new initiatives.</strong> Join design and solution-concept meetings with R&D before code is written and help shape architectures that are secure by default. Produce threat models, reference designs, and decision docs that engineers actually use.</li> <li><strong>Partner with R&D as a peer, not a gatekeeper.</strong> Embed with product and engineering teams. Translate security requirements into engineering language, and engineering trade-offs into risk language for leadership.</li> <li><strong>Own security across our multi-cloud footprint</strong> (AWS, GCP, Azure). Design and implement controls for identity, network, workload, and data protection that work consistently across environments.</li> <li><strong>Drive AI security.</strong> Build our defenses against threats specific to LLM and ML systems: prompt injection, model abuse, training-data integrity, agentic-workflow risks, MCP/tool-use exposure, secrets in prompts, and supply-chain risk in the AI stack. Help define what "secure AI development" means for us in practice.</li> <li><strong>Lead data security initiatives.</strong> Classification, encryption, key management, DLP, access governance, and data-flow mapping.</li> <li><strong>Evaluate and implement new technologies.</strong> Run POCs and deploy new tooling end-to-end. We expect you to have strong opinions on the security stack and to evolve it as the threat landscape shifts.</li> <li><strong&gt