About Centific

Centific is a frontier AI data foundry that curates diverse, high-quality data, using our purpose-built technology platforms to empower the Magnificent Seven and our enterprise clients with safe, scalable AI deployment. Our team includes more than 150 PhDs and data scientists, along with more than 4,000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem—comprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 markets—to create contextual, multilingual, pre-trained datasets; fine-tuned, industry-specific LLMs; and RAG pipelines supported by vector databases. Our zero-distance innovation™ solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster.

Our mission is to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers. We aim to help these organizations unlock significant business value by deploying GenAI at scale, helping to ensure they stay at the forefront of technological advancement and maintain a competitive edge in their respective markets.

About Job

Our Vision AI platform runs where the data is generated — on-premises, inside government facilities, and at the network edge — not in a hyperscaler cloud. That means the infrastructure has to be bulletproof: GPU clusters provisioned correctly, Kubernetes workloads scheduled efficiently across heterogeneous compute, storage performing at the throughput AI training and inference demands, and the network capable of handling high-bandwidth, low-latency sensor data at scale.

As our MLOps / AI Infrastructure Engineer, you will own all of it. You will rack, configure, and operate the on-premises compute and GPU infrastructure that powers the platform, build and maintain the Kubernetes clusters that orchestrate AI workloads, design the networking fabric that ties edge nodes to core compute, and implement the MLOps pipelines that take models from development to production. You will work directly with our AI/ML engineers, the Lead Architect, and on-site client technical teams to ensure the platform runs reliably in environments that are often air-gapped, physically secured, and subject to strict government compliance requirements.

GPU Compute & Hardware Infrastructure

• Deploy, configure, and maintain on-premises GPU servers — primarily NVIDIA H200 and A100 nodes — including driver management, CUDA toolkit versioning, NVLink/NVSwitch topology, and firmware updates.
• Implement and tune NVIDIA-specific tooling: DCGM (Data Center GPU Manager) for health monitoring and telemetry, MIG (Multi-Instance GPU) partitioning for multi-tenant workloads, and NVIDIA Container Toolkit for GPU-aware containerization.
• Manage bare-metal provisioning workflows (iPXE, PXE, or tools such as MAAS/Foreman) to enable repeatable, auditable server builds at client sites.
• Monitor hardware health, capacity utilization, and thermal/power envelopes; define alerting thresholds and respond to hardware failures with minimal service disruption.

Kubernetes & Container Orchestration

• Build, upgrade, and maintain production-grade Kubernetes clusters (kubeadm or Rancher RKE2) on bare-metal infrastructure, with GPU node pools configured via the NVIDIA GPU Operator.
• Design and operate cluster networking using CNI plugins appropriate for high-throughput AI workloads — Calico, Cilium, or SR-IOV for RDMA-capable networking where required.
• Configure and manage MetalLB or equivalent bare-metal load balancing, ingress controllers, and service mesh components (Istio or Linkerd) for secure intra-cluster communication.
• Implement resource quotas, LimitRanges, PriorityClasses, and node affinity/taints to ensure AI training jobs, inference services, and platform workloads coexist without resource contention.
• Maintain cluster security posture: RBAC policies, Pod Security Admission, network policies, secrets management (HashiCorp Vault or Sealed Secrets), and CIS Kubernetes Benchmark compliance.

MLOps Pipelines & AI Workload Management

• Deploy and operate MLOps platforms (MLflow, Kubeflow, or equivalent) for experiment tracking, model versioning, and pipeline orchestration across training and inference workloads.
• Configure and manage NVIDIA Triton Inference Server for multi-model serving, dynamic batching, and model ensemble execution on GPU nodes.
• Build CI/CD pipelines for model deployment (GitOps with ArgoCD or Flux), including automated model validation, canary rollouts, and rollback mechanisms.
• Optimize GPU utilization for both batch training jobs (Volcano or KUEUE scheduler) and latency-sensitive inference services, tracking efficiency metrics via DCGM and Prometheus.
• Manage model artifact storage and versioning using software-defined storage backends (Ceph RBD/CephFS or MinIO) integrated with the MLOps toolchain.

Networking & Storage Architecture

• Design and implement the high-bandwidth network fabric required for GPU cluster interconnects — InfiniBand, RoCE v2, or high-speed Ethernet — and ensure RDMA is correctly configured for distributed training workloads.
• Deploy and operate software-defined storage solutions (Ceph or equivalent) providing block, object, and file storage tiers for training datasets, model checkpoints, and platform telemetry.
• Configure network segmentation, VLANs, and firewall policies to meet NIST 800-171 requirements in on-premises and air-gapped environments; document network topology for client system security plans.
• Establish and maintain VPN or secure tunneling solutions for hybrid connectivity between edge nodes, on-premises clusters, and any permitted cloud services.

Security, Compliance & Documentation

• Implement infrastructure controls mapped to NIST SP 800-171 and CMMC requirements: access control, audit logging, configuration management, incident response readiness, and media protection.
• Maintain hardened OS baselines (RHEL/Rocky STIG or Ubuntu CIS benchmarks) across all infrastructure nodes; automate compliance scanning with OpenSCAP or equivalent.
• Produce and maintain infrastructure documentation required for government procurement: network diagrams, hardware inventories, system security plan (SSP) contributions, and disaster recovery runbooks.
• Support penetration testing engagements by providing accurate infrastructure context and remediating findings within agreed timelines.

• 6+ years of infrastructure engineering experience, with at least 3 years managing GPU compute clusters or HPC environments in production.
• Deep hands-on expertise with NVIDIA GPU infrastructure: driver lifecycle management, CUDA, DCGM, MIG, NVLink topologies, and the NVIDIA GPU Operator for Kubernetes.
• Production-level Kubernetes administration experience on bare-metal: cluster provisioning, upgrades, CNI/CSI configuration, RBAC, and day-2 operations.
• Strong networking fundamentals: BGP, VLAN segmentation, RDMA/RoCE or InfiniBand configuration, load balancing, and firewall policy management.
• Hands-on experience with software-defined storage (Ceph, Rook-Ceph, or MinIO) in AI/HPC workload contexts — performance tuning, capacity planning, and failure recovery.
• Practical MLOps experience: model serving infrastructure (Triton or equivalent), experiment tracking (MLflow or Kubeflow), and GitOps-based model deployment pipelines.
• Working knowledge of NIST SP 800-171 controls and the ability to translate them into concrete infrastructure configurations and audit evidence.
• Proficiency with infrastructure-as-code tooling: Terraform or Ansible for reproducible, auditable infrastructure builds.
• Strong Linux systems administration skills (RHEL/Rocky Linux or Ubuntu) including kernel tuning, storage I/O optimization, and systemd service management.
• Excellent written communication for producing infrastructure runbooks, network diagrams, and compliance documentation in a remote-first environment.

• Experience with air-gapped or classified network environments and the operational discipline they require (offline package mirrors, USB-controlled media transfers, etc.).
• Familiarity with CMMC Level 2/3 assessment processes and evidence collection.
• Experience with NVIDIA DGX Systems, BasePOD reference architectures, or NVIDIA AI Enterprise software stack.
• Knowledge of distributed training frameworks (PyTorch DDP, DeepSpeed, Megatron-LM) and their infrastructure requirements — useful for supporting AI/ML engineering teammates.
• Experience deploying Kubernetes at the edge: K3s, MicroK8s, or NVIDIA Jetson-based edge clusters.
• Familiarity with observability stacks: Prometheus, Grafana, Loki, OpenTelemetry, and DCGM Exporter for GPU telemetry dashboards.
• US Person status or active security clearance — advantageous for certain client site engagements.
• Background in SCADA, ICS, or OT network environments relevant to critical infrastructure clients.

• Hands-on ownership of some of the most demanding AI infrastructure in the public sector — H200 GPU clusters, high-bandwidth interconnects, and purpose-built on-premises deploymen