Role Description As a Lead AI Platform Engineer, you will be the backbone of our AI production lifecycle. You will bridge the gap between research and real-world application, ensuring our Data Scientist, AI Researchers, Product teams and others in the company have the high-performance infrastructure, automated pipelines, and deployment strategies needed to ship state-of-the-art models and agents at scale. Qualifications - 5+ years experience with cloud infrastructure and infrastructure as code. - Previous experience with the ML and LLM lifecycle - training, hosting, optimisation, observability. - Used to working closely with researchers and data scientists - taking experiments from worksheets into production. - Strong grasp of ML fundamentals and modern GenAI stack. Requirements - Infrastructure as Code (IaC): Design and maintain scalable cloud environments (GCP/AWS) using Terraform. - Resource Provisioning: Manage GPU/TPU resource allocation for training, fine-tuning, and interactive notebooks. - Custom Tooling: Build internal services and CLI tools to streamline the developer experience for the AI team. - Automated Pipelines: Design CI/CD and training pipelines using tools such as GitHub Actions, MLFlow, Vertex AI Pipelines. - Deployment Methodology: Develop reusable patterns for model serving. Managing service deployments to Kubernetes. - Vector Infrastructure: Manage and optimize vector databases and embedding pipelines for RAG-based systems. - Observability and Reliability: Model drift monitoring, resource utilisation, LLM and agent tracing. - Inference Optimization: Implement techniques to reduce latency and increase throughput (quantisation, distillation, etc…) - Cold Start Mitigation: Solve scaling bottlenecks for serverless or containerized model deployments. - Cost Management: Optimize GPU utilization and cloud spend without compromising performance. - Support AI Agent Deployment: Define and create tooling and service templates around agent deployment (tool libraries, tracing, default agent frameworks, skills, etc…). - Enablement for non-technical agent users: Help create workflows and guidance on no-code/low-code agent platforms (n8n, LangSmith, or similar). - Create tooling and policies to enable safe usage of local agents such as Claude code. Benefits - Competitive salary. - Benefits. - Remote working within an impactful, mission-driven culture.

• Design and implement the AWS platform foundations used by product and service teams across RWS • Develop reusable infrastructure patterns aligned with the RWS platform reference architecture • Implement core cloud capabilities including networking, identity integration, security controls, and platform services • Support the creation of standardised infrastructure building blocks to accelerate application deployment • Support engineering and IT teams with guidance as migration of application workloads from on-premise environments into AWS is completed • Build and implement prioritised plan for migrated applications • Collaborate with application teams to modernise architectures • Provide guidance and tooling to help teams successfully adopt AWS infrastructure and services • Build and maintain infrastructure using Infrastructure as Code to ensure consistent, repeatable cloud deployments • Enable product teams to provision infrastructure and deploy services through self-service platform capabilities

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