Rackner

• Develop secure APIs and cloud-native services for DHA systems • Build and maintain data pipelines to normalize structured and unstructured data • Integrate healthcare data into FHIR and OMOP standards for analytics and decision support • Collaborate with DevSecOps engineers to deploy applications in secure, high-compliance cloud environments • Ensure software is scalable, compliant, and maintainable while enabling rapid delivery

• Build and maintain end-to-end Ingest → Transform → Expose pipelines using Airflow, Spark, dbt, and Iceberg. • Ingest and normalize structured and unstructured data (HL7, FHIR, PDF, JSON) for analytics and AI/ML use cases. • Map datasets to FHIR and OMOP standards to enable interoperability and decision support. • Implement schema versioning and governance to ensure traceability and audit-ready lineage. • Collaborate with DevSecOps and Data Science teams to deliver AI-ready datasets for predictive analytics and readiness forecasting. • Optimize data performance across distributed environments while ensuring compliance with DoD Responsible AI and NIST AI Risk Management frameworks.

• Partner with program managers, domain experts, and end users to gather and clarify business and functional data requirements. • Document requirements, data definitions, and business rules so technical teams understand what is needed and why. • Translate stakeholder needs into clear deliverables such as requirements documentation, user stories, and acceptance criteria. • Revisit and refine requirements as priorities or feedback evolve. • Review existing data workflows and business processes to identify inefficiencies, delays, or manual workarounds. • Help shape improvements that streamline operations and improve data availability and usability. • Ensure analytics and reporting align with how the organization actually operates. • Analyze integrated datasets to produce insights that support operational and leadership decisions. • Build and maintain dashboards and reports using Tableau or Qlik, with a focus on clarity and usability. • Write and validate SQL queries to support analysis, reporting logic, and data verification. • Ensure metrics are well-defined, accurate, and consistent across reports. • Work with technical and QA teams to confirm data outputs meet business expectations. • Perform high-level validation to ensure results make sense in context, not just technically. • Help identify gaps, inconsistencies, or definition issues before data is delivered to stakeholders. • Create and maintain documentation including requirements specifications, data dictionaries, and report user guides. • Communicate findings clearly to non-technical audiences, translating data into practical insight.

• Design, build, and maintain the data storage and integration components of a mission-critical federal program. • Ensure enterprise databases are well-designed, high-performing, and reliable to support data integration objectives. • Design and implement database schemas and features, including complex SQL queries, stored procedures, and database code. • Monitor and optimize database performance through query tuning, indexing, and resource management. • Perform database optimization, performance tuning, and configure high-availability solutions to support large data volumes. • Oversee routine maintenance tasks such as backups, recovery procedures, and patching. • Implement and maintain robust disaster recovery strategies and backup policies. • Enforce database security controls, including access management, encryption, and hardening procedures. • Collaborate closely with data engineers and integration developers to ensure database structures support ETL/ELT processes. • Validate that database schemas align with incoming data feeds and ensure smooth operation of interfaces used for data integration.

• Build, prototype, test, and refine software and platform solutions for defense-relevant use cases • Contribute to internal R&D efforts from concept through demonstration and follow-on improvement • Develop, configure, integrate, and validate prototype capabilities across cloud-native or containerized environments • Support technical demos, R&D events, solution walkthroughs, and customer or mission-focused engagements • Translate user, customer, and stakeholder feedback into clear technical requirements and next-step recommendations • Work closely with engineers and technical leadership to evaluate tradeoffs, troubleshoot issues, and improve system behavior • Bridge operational needs and software implementation by helping teams understand what users need and what is technically feasible • Test APIs, services, workflows, integrations, and deployment patterns in modern software environments • Prepare demo materials, technical documentation, briefing content, and post-event feedback summaries • Communicate technical concepts clearly to engineering teams, program stakeholders, customers, and mission users • Represent Rackner’s R&D work with professionalism, curiosity, and technical credibility

• Lead the design, prototyping, and development of mission-relevant software and platform capabilities • Build backend services, APIs, full-stack applications, and cloud-native components using modern engineering practices • Translate ambiguous mission needs, user workflows, and technical concepts into working software • Support rapid build, demo, feedback, and iteration cycles for R&D initiatives • Collaborate directly with technical leadership, R&D engineers, and stakeholders on architecture and implementation decisions • Participate in technical demos, customer-facing discussions, and mission-focused events • Help shape system design, platform direction, and technical tradeoffs across emerging capabilities • Guide or mentor other engineers through architecture decisions, prototyping efforts, and implementation challenges • Deploy and support applications in cloud-native, containerized, or Kubernetes-based environments • Use modern development workflows, including AI-assisted tools where appropriate, to accelerate prototyping, testing, and delivery • Represent Rackner’s technical work with clarity, professionalism, and credibility in collaborative settings

Role Description Rackner is seeking a DevSecOps (Kubernetes) Engineer SME to support a US Air Force program called Platform One, to work on a product called Big Bang. Big Bang provides the tooling for mission application owners to create a Platform as a Service in their own Kubernetes cluster running in a cloud or datacenter. This PaaS environment is built with Terraform and Helm charts, and comes with cybersecurity and other governance policies out of the box, as well as collaboration tools and plugins. We're looking for a DevSecOps Engineer who has deep experience in Kubernetes, Terraform and CI/CD Pipelines to join our team. This role is remote. Qualifications - 3+ years of Kubernetes experience in production environments - Experience with Kubernetes distributions (RKE2, EKS, OpenShift, VMWare Tanzu, etc.) - 3+ years of Terraform experience - Experience with Docker or other container technologies - Experience with Helm - Background with defense customers (highly preferable) Requirements - Build DevSecOps platforms which are used by a variety of mission application owners - Create and update Kubernetes clusters using Terraform - Deploy applications to Kubernetes clusters by writing and modifying Helm charts - Become comfortable getting exposed to and learning different Kubernetes distributions (RKE2, OpenShift, Tanzu, etc.) - Self-teach and work with custom operators and CRDs in Kubernetes as needed - Use automation best practices, such as IaC and GitOps, for repeatability and fast provisioning of environments - Ensure platform and pipelines are compliant with DoD cybersecurity policies (NIST 800-53/RMF, STIGs) - Work with the Cybersecurity team by implementing industry best practices for system hardening and configuration management Benefits - Employee development and training with coverage for certifications relevant to the position and technologies/services provided - Fitness/Gym membership eligibility - Weekly pay schedule - Employee swag, snacks & events - 401K with 100% matching up to 6% - Highly competitive PTO - Great health insurance with a large network of providers - Medical/Dental/Vision coverage - Life Insurance, and short & long term disability - Industry-Leading Weekly Pay Schedule - Home office & equipment plan

• The Kubernetes SME will work closely with the Infrastructure as Code and Site Reliability Engineer SMEs to design, build, and update platform capabilities using Kubernetes native services adhering to Cloud Native Computing Foundation (CNCF) principles. • The Kubernetes SME will ensure industry best practices, along with the DoD CIO DevSecOps Reference Design, and a loosely coupled microservices-based architecture. • Work to further develop, deliver, field, secure and maintain Unified Platform’s Kubernetes-based DevSecOps platform.

• Rackner is looking for a Software Engineer to develop and deliver first class solutions within a DevSecOps environment using latest cloud-native technologies to architect and implement containerized applications, CI/CD pipelines, and Kubernetes platforms using best practices and leading technologies with a Secret clearance or eligibility to obtain one. • As part of an Agile team, use DevSecOps best practices to rapidly develop and deliver first class solutions for a DoD customer. • Develop software using leading languages and frameworks including Python and FastAPI, AWS, and Rancher Kubernetes.

Role Description At Rackner, we build systems where advanced technologies move beyond prototypes and into real-world operational use. We are seeking an MLOps Engineer to support the deployment and lifecycle management of AI/ML systems within a secure, mission-focused environment. This is not a research role. This is where models become reliable, deployable, and auditable systems. You will operate at the intersection of: - machine learning - cloud-native infrastructure - distributed systems …and ensure AI/ML systems are production-ready in environments where reliability and performance matter. What You’ll Do - Own the ML Lifecycle (End-to-End) - Build and operate production-grade ML pipelines - Orchestrate workflows using Kubeflow, Airflow, or Argo - Implement model versioning, lineage, and reproducibility standards - Operationalize AI/ML Systems - Deploy models into secure and constrained environments - Transition workflows from experimentation → containerized pipelines → production systems - Enable both batch and real-time inference architectures - Engineer for Reliability - Design systems for reproducibility, auditability, and stability - Monitor model performance and system health using Prometheus, Grafana, OpenTelemetry - Detect and resolve issues such as model drift and system degradation - Build Cloud-Native ML Infrastructure - Deploy and manage Kubernetes-based ML workloads - Containerize pipelines using Docker - Support scalable training and inference workflows - Establish Data Discipline - Support feature engineering and dataset preparation - Implement data versioning and governance practices (e.g., lakeFS) - Apply metadata and data management standards - Create Repeatable Systems - Develop runbooks, playbooks, and documentation - Build systems that are operationally sustainable and transferable Qualifications - Experience deploying ML systems into production environments - Strong programming skills in Python - Hands-on experience with: - ML pipeline tools (Kubeflow, Airflow, Argo) - Experiment tracking tools (MLflow, ClearML) - Experience with Kubernetes and containerized systems (Docker) - Familiarity with CI/CD pipelines - Understanding of distributed systems and scalable architectures - Experience working with: - LLMs or transformer-based models - Computer vision systems (YOLO, Faster R-CNN) - Focus on deployment and integration, not pure research Mindset - Systems thinker who prioritizes reliability over novelty - Comfortable operating in complex, evolving environments - Focused on delivering real-world outcomes Clearance Requirements - Active TS/SCI clearance strongly preferred - Candidates with an active Secret clearance may be considered and supported for upgrade - Candidates without an active clearance must be: - U.S. citizens - Eligible to obtain and maintain a clearance - Able to work in a CAC-enabled or secure environment Note: Start timelines and work scope may vary depending on clearance status and program requirements. Benefits - 100% covered certifications & training aligned to your role - 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 Company Description Rackner is a software consultancy that builds cloud-native solutions for startups, enterprises, and the public sector. We are an energetic, growing team focused on solving complex problems through: - Distributed systems - DevSecOps - AI/ML - Cloud-native architecture Our approach is cloud-first, cost-effective, and outcome-driven, delivering systems that scale and perform in real-world environments. If you’re an engineer who wants to move from building models → owning production systems, we’d like to connect.