Platform Engineer (Cloud-Native AI/ML Systems Integration) Location: Dayton, OH (preferred) | Remote Eligible (CAC Access Required) Clearance: TS/SCI Preferred Build the Infrastructure That Makes AI Mission-Ready This is not a typical engineering role. As a Platform Engineer, you will design and operate the infrastructure that enables AI/ML systems to function in real-world mission environments. Your work will directly support Air Force / NASIC-aligned programs, where performance, security, and reliability are non-negotiable. You won’t just build platforms: you’ll bridge the gap between AI development and operational deployment, ensuring systems scale, integrate, and perform under real constraints. What You’ll Do - Architect and operate Kubernetes-based platforms supporting AI/ML workloads - Build and manage containerized environments (Docker, Helm, OCI) for scalable deployment - Design and optimize data pipelines for ingestion, transformation, and model lifecycle support - Integrate AI/ML services into secure, mission-critical systems - Develop Infrastructure as Code (Terraform, Ansible) for repeatable, compliant environments - Build and enhance CI/CD pipelines (GitLab, Jenkins, GitHub Actions) - Implement observability and monitoring (Prometheus, Grafana) to ensure system health and performance - Collaborate cross-functionally to translate complex requirements into deployable systems - Solve engineering challenges within classified and constrained environments What You Bring - Strong experience with Kubernetes and cloud-native platform engineering - Hands-on experience with containerization (Docker, Helm) - Experience supporting data pipelines or ML-enabled systems - Familiarity with Infrastructure as Code (Terraform, Ansible) - Experience with CI/CD and DevSecOps practices - Understanding of distributed systems and system integration - Ability to operate effectively in secure or regulated environments Why This Role Matters In many environments, AI stops at experimentation; in this role, you ensure it becomes operational capability. You will: - Enable AI/ML systems to move from development to deployment - Support mission-critical operations tied to national security - Work at the intersection of cloud-native engineering, DevSecOps, and AI infrastructure - Build systems where failure is not an option and performance is essential Your work directly impacts how advanced technology is applied in real-world scenarios. What You’ll Gain - Hands-on ownership of platforms powering AI/ML in mission environments - Exposure to complex, high-scale distributed systems - Experience integrating modern cloud-native technologies into secure, real-world systems - Growth across platform engineering, DevSecOps, and AI infrastructure - The opportunity to solve problems most engineers never encounter About Rackner Rackner is a software consultancy that builds cloud-native solutions for startups, enterprises, and the public sector. We are an energetic, growing consultancy focused on solving complex problems through distributed systems, DevSecOps, AI/ML, and modern systems architecture. We enable digital transformation by applying cloud-first, cost-effective innovation across mission-critical environments. Our customers span a diverse and growing set of industries, and our teams are driven by a shared focus on end-to-end system delivery and real-world impact. Benefits & Perks Rackner invests in your growth, stability, and long-term success: - Paid certifications & professional development - 401(k) with 100% match up to 6% - Highly competitive PTO - Comprehensive Medical, Dental, Vision coverage - Life Insurance + Short & Long-Term Disability - Home office & equipment plan - Industry-leading weekly pay schedule #PlatformEngineering #Kubernetes #DevSecOps #AIInfrastructure #MachineLearningOps #CloudNative #DefenseTech #ClearedJobs #NationalSecurity #InfrastructureEngineering #DistributedSystems #Terraform #Docker #DataEngineering #MLOps

• Own the design, governance, and continuous evolution of UniUni's AWS-based cloud platform • Lead and define cloud architecture standards across all AWS services • Own end-to-end FinOps practice including budgeting and forecasting • Mandate and mature IaC-first practices across the organization • Own the strategy, performance, and reliability of UniUni's multi-model database platform • Establish and enforce cloud security baselines • Lead, coach, and grow a team of platform and infrastructure engineers

• Design, implement, and maintain cloud infrastructure in AWS with attention to scalability, reliability, security, and automation • Manage infrastructure using Infrastructure as Code, including hands-on work with Terraform and experience with CloudFormation • Deploy, administer, and maintain Kubernetes clusters in production environments • Improve platform tooling, deployment workflows, and operational practices that support engineering teams and production systems • Implement and manage observability and monitoring solutions, ideally including experience with Datadog, across metrics, logging, alerting, and incident response • Work with containerized systems and deployment workflows, including image build pipelines and optimization techniques • Create and maintain CI/CD pipelines, including required experience with GitHub Actions; experience with Jenkins and/or ArgoCD is a plus • Partner with teammates across engineering to identify needs, evaluate options, and deliver practical infrastructure and platform solutions • Investigate platform and infrastructure issues, support root-cause analysis, and contribute to sustainable improvements • Participate in technical planning, architecture discussions, and continuous improvement efforts across the platform engineering function

• Build advanced AI agent workflows on n8n and Supernal's proprietary platform • Design, implement, and deploy conversational agents, including multi-turn flows, state management, and tool usage • Own end-to-end technical delivery for high-priority customer implementations, from architecture through production launch • Translate customer requirements and SOWs into clear technical designs, execution plans, and deliverables • Make and own architectural decisions across LLM orchestration, RAG design, API integrations, and workflow decomposition • Handle real-world voice system challenges including latency, interruptions, fallbacks, error handling, and failure recovery • Write automated tests — unit tests for isolated logic and end-to-end tests for full workflow and user journey validation • Apply solid error handling: distinguish retryable vs. fatal failures, surface meaningful error messages, and avoid silent failures • Actively debug complex production issues across agent logic, prompts, integrations, and external dependencies • Partner with delivery and product leadership to manage timelines, scope, and technical tradeoffs during implementation • Review technical work for quality, scalability, and maintainability, setting a high bar for engineering excellence • Define, document, and evolve best practices for building and delivering reliable AI Employees