MLOps Engineer AI/ML Systems & Deployment (TS/SCI Preferred)

MLOps Engineer — AI/ML Systems & Deployment (TS/SCI Preferred)Dayton, OH (On-site Preferred) | Remote Eligible (CAC-Ready Candidates)Mission Environment | AI/ML Infrastructure | National Security ImpactAbout the RoleAt Rackner, we are building the operational backbone that turns AI/ML capability into real-world mission outcomes. We are seeking an MLOps Engineer to own the lifecycle of AI/ML systems—from experimentation to deployment—within a mission-critical, classified environment supporting Air Force and NASIC-aligned programs.This is not a research role; this is where models become reliable, deployable, auditable systems.You will operate at the intersection of:Machine learningDistributed systemsCloud-native infrastructure…and ensure that AI/ML systems work in the environments where failure is not an option.What You'll DoOwn the ML Lifecycle (End-to-End)Build and operate production-grade ML pipelinesOrchestrate workflows using Kubeflow, Airflow, or ArgoImplement model versioning, lineage, and reproducibility standardsOperationalize AI/ML SystemsDeploy models into mission environments (including constrained or classified systems)Transition workflows from Jupyter experimentation → containerized pipelines → production systemsEnable both batch and real-time inference architecturesEngineer for Reliability, Not Just PerformanceDesign systems for reproducibility, auditability, and stabilityImplement monitoring for:model performance & driftsystem health & latencyUse tools like Prometheus, Grafana, and OpenTelemetryBuild Cloud-Native ML InfrastructureDeploy and manage Kubernetes-based ML workloadsContainerize pipelines using Docker / OCI standardsScale compute for training and inference workloadsEstablish Data DisciplineEnable data versioning and governance (lakeFS or similar)Support feature engineering and dataset preparation pipelinesApply metadata standards (e.g., STAC) where applicableCreate Repeatable SystemsDevelop runbooks, playbooks, and deployment standardsBuild systems that can be operated by others; not just understood by youWhat You BringCore ExperienceExperience deploying ML systems into production environmentsStrong background in Python and ML frameworks (PyTorch, TensorFlow, etc.)Hands‑on experience with:ML pipeline orchestration tools (Kubeflow, Airflow, Argo)Experiment tracking (MLflow, ClearML)Infrastructure & SystemsExperience with Kubernetes and containerized workloadsFamiliarity with CI/CD for ML systemsUnderstanding of distributed systems and scalable architecturesML Application ExposureExperience working with:LLMs or transformer-based modelsComputer vision systems (YOLO, Faster R‑CNN)Focus on deployment and integration, not pure researchMindsetSystems thinker who values reliability over noveltyComfortable operating in ambiguous, high‑stakes environmentsAble to translate experimental work into operational capabilityWhy This Role Matters (What You Get)Move beyond experimentationOwn systems that actually get deployed and usedOperate at the systems levelWork across ML, infrastructure, and mission integrationBuild in high‑trust environmentsWhere correctness, auditability, and reliability matterDevelop rare, high‑demand expertiseMLOps in constrained / classified environments is a differentiated skillsetShape how AI is operationalized—not just builtWho We AreRackner is a software consultancy that builds cloud-native solutions for startups, enterprises, and the public sector. We are an energetic, growing consultancy with a passion for solving big problems across industries.We enable digital transformation through:Distributed systemsDevSecOpsAI/MLCloud-native architectureOur approach is cloud‑first, cost-effective, and outcome-driven—focused on delivering real capability, not just code.Benefits & Perks100% covered certifications & training aligned to your role401(k) with 100% match up to 6%Highly competitive PTOComprehensive Medical, Dental, Vision coverageLife Insurance + Short & Long-Term DisabilityHome office & equipment planIndustry-leading weekly pay scheduleApplyIf you're an engineer who wants to move from building models → owning systems, we want to talk.#J-18808-Ljbffr

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