Senior Security Engineer

Job description

**Who is Eleos Health?** The people who deliver care in our communities and homes do some of the most important work in healthcare. But too often, theyʼre asked to spend their time navigating paperwork, administrative burden, and problems they only discover after something has already gone wrong. Eleos exists to change that. Weʼre building the system of action for community-based care - helping behavioral health, hospice, home health, and other care teams spend less time reacting to problems and more time focused on the people they serve. Powered by our Polaris AI, Eleos works alongside clinicians and their teams to understand whatʼs happening across care in real time and take action before issues become problems, reducing administrative burden, protecting revenue and compliance, and giving care teams more time and capacity to deliver great care. Our vision is a world where care teams can stay one step ahead, rather than constantly catching up. Weʼre helping shift an entire industry from reactive to proactive, and we need talented people to help us get there. ## **What is this opportunity?** 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. This role suits an engineer who thinks in systems, codes when needed, and is energized by securing modern AI-driven, multi-cloud environments. ## **What you'll do** - **Act as a security architect for new initiatives.** 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. - **Partner with R&D as a peer, not a gatekeeper.** Embed with product and engineering teams. Translate security requirements into engineering language, and engineering trade-offs into risk language for leadership. - **Own security across our multi-cloud footprint** (AWS, GCP, Azure). Design and implement controls for identity, network, workload, and data protection that work consistently across environments. - **Drive AI security.** 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. - **Lead data security initiatives.** Classification, encryption, key management, DLP, access governance, and data-flow mapping. - **Evaluate and implement new technologies.** 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. - **Write code.** Automate controls, build internal tools, contribute to detection pipelines, and integrate security into CI/CD. **What qualifications and skills will help you to be successful?** **Must-haves** - 5+ years in security engineering, application security, or cloud security, with meaningful time in a hands-on technical capacity - Strong software development background — you've shipped production code and read others' code fluently. - Deep experience securing multi-cloud environments, including IAM, networking, workload security, and infrastructure-as-code - Architect-level thinking: ability to look at a system diagram and identify trust boundaries, blast radius, failure modes, and the controls that actually matter - Hands-on experience with data security at scale, classification, encryption, key management, access controls - Demonstrated experience introducing and operationalizing new security technologies, not just maintaining existing ones - Track record of working closely with engineering teams; you're comfortable in design reviews and code reviews - Direct experience with AI/ML security, securing LLM applications, agentic systems,model deployments, or AI infrastructure