ai infrastructure engineer

Dautom

<div>
 <p>
  The AI Infrastructure Engineer is a platform specialist responsible for architecting, building, and operating high-performance AI infrastructure to support advanced AI workloads, including LLMs, GenAI, Computer Vision, and MLOps. This role will focus on managing GPU clusters (NVIDIA A100/H100), deploying and maintaining Red Hat OpenShift AI (RHODS), and ensuring secure, scalable, and cost-efficient AI platforms across SDD&rsquo;s Sovereign Cloud and hybrid/multi-cloud environments. The engineer will enable enterprise-grade AI adoption for 200+ government entities.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Key Responsibilities &amp; Deliverables
 </p>
 <p>
  <br/>
 </p>
 <p>
  GPU &amp; AI Platform Architecture
 </p>
 <p>
  Design and implement GPU-based compute clusters. Define reference architectures for LLM hosting, Vector Databases, MLOps, and high-performance storage/networking.
 </p>
 <p>
  Fully operational GPU-based AI infrastructure. GPU Cluster Uptime and Performance Utilization. Reduction in Cost per Training/Inference Workload.
 </p>
 <p>
  GPU Cluster Operations
 </p>
 <p>
  Install, configure, and optimize core components: CUDA, cuDNN, NCCL, NVIDIA Drivers, and GPU Operators. Implement GPU partitioning, scheduling, and performance tuning for high-end GPUs (e.g., A100/H100).
 </p>
 <p>
  High-availability architecture for all AI workloads. Complete documentation and runbooks.
 </p>
 <p>
  OpenShift AI (RHODS) Management
 </p>
 <p>
  Deploy, configure, and maintain the Red Hat OpenShift AI (RHODS) platform for multi-tenant use. Manage the integration of NVIDIA GPU Operator for efficient GPU scheduling and support Data Scientists with Notebooks, Training, and Inference Endpoints.
 </p>
 <p>
  Production-ready OpenShift AI (RHODS) platform. AI Project Onboarding Speed.
 </p>
 <p>
  LLM &amp; Model Serving
 </p>
 <p>
  Build and manage infrastructure for hosting and serving open-source LLM frameworks (Llama, Falcon, Mistral) and supporting RAG pipelines, LoRA adapters, and Vector Databases (Milvus, pgvector).
 </p>
 <p>
  Multi-model LLM serving environment for entities. MLOps Pipeline Success Rate and Deployment Frequency.
 </p>
 <p>
  MLOps &amp; Automation
 </p>
 <p>
  Implement IaC (Terraform, Ansible) and GitOps for the automated lifecycle management of the AI platform (node onboarding, scaling, model rollout/rollback). Build robust MLOps pipelines for data prep, training, evaluation, and monitoring (using tools like MLflow/Kubeflow).
 </p>
 <p>
  Infrastructure automation via Terraform &amp; Ansible. Automation Coverage for AI Infrastructure.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Required Qualifications &amp; Experience
 </p>
 <ul>
  <li>
   Experience: 7&ndash;12 years in Cloud Infrastructure, DevOps, ML Infrastructure, or Platform Engineering.
  </li>
  <li>
   Deep Hands-On Expertise:
  </li>
  <li>
   GPU Systems (NVIDIA A100/H100), Linux, Containers, and Kubernetes.
  </li>
  <li>
   OpenShift AI (RHODS) or equivalent Kubernetes GPU orchestration.
  </li>
  <li>
   LLM Hosting (Llama, Mistral, Falcon, etc.) and supporting Vector Databases/RAG systems.
  </li>
  <li>
   Strong Experience In: TensorFlow, PyTorch, Hugging Face, Distributed Training (DDP, Deep Speed), and ML Ops Stacks (ML flow, Kubeflow).
  </li>
 </ul>
 <p>
  Essential Skills &amp; Competencies
 </p>
 <ul>
  <li>
   Technical: Deep understanding of GPU compute, HPC architectures, and ML performance profiling. Strong skills in IaC (Terraform/Ansible), CI/CD, and OpenShift/Kubernetes operators.
  </li>
  <li>
   Soft Skills: Strong troubleshooting, optimization, and performance engineering mindset. Excellent cross-functional collaboration and documentation skills.
  </li>
 </ul>
 <p>
  Preferred Certifications
 </p>
 <ul>
  <li>
   NVIDIA Deep Learning / AI Infrastructure Certification
  </li>
  <li>
   Red Hat OpenShift AI specialization
  </li>
  <li>
   Kubernetes CKA/CKAD
  </li>
  <li>
   Azure AI or Oracle Cloud AI certifications
  </li>
  <li>
   Terraform &amp; Ansible certifications
  </li>
 </ul>
 <p>
  <br/>
 </p>
</div>


AI Infrastructure Engineer

RCV Innovations Pvt Ltd

<p><b>Job Description </b></p><p>​We are seeking a highly skilled and hands-on <b>AI Infrastructure Engineer</b> to design, build, and maintain our next-generation, self-hosted AI infrastructure. In this role, you will bridge the gap between enterprise infrastructure and advanced AI engineering. You will be responsible for deploying, optimizing, and orchestrating multi-model Large Language Models (LLMs), Vision-Language Models (VLMs), and Agentic AI frameworks on both raw bare-metal hardware and enterprise container platforms.</p><p>​The ideal candidate thrives in a high-performance computing environment, understands the nuances of bare-metal GPU clustering, and has a proven track record of moving open-source AI stacks from experimental setups into secure, scalable production environments.</p><p><b>​Key Responsibilities</b></p><p><b>​1. AI Infrastructure &amp; Deployment Orchestration</b></p><ul><li>​Design, provision, and manage high-performance compute clusters on <b>bare-metal (Rocky Linux / Ubuntu)</b> and enterprise platforms (<b>Red Hat OpenShift / Kubernetes</b>).</li><li>​Deploy, optimize, and maintain scalable LLM/VLM serving engines using <b>vLLM</b> and <b>Ollama</b>, ensuring low-latency and high-throughput inference.</li><li>​Configure and scale distributed training and inference workloads using <b>Ray framework</b>, specifically optimizing for multi-GPU, multi-modal applications (Text, Image, and Audio).</li><li>​Set up, secure, and customize enterprise-grade user interfaces like <b>Open WebUI</b> for cross-functional team access.</li></ul><p><b>​2. Advanced RAG &amp; Model Lifecycle Management</b></p><ul><li>​Architect and maintain end-to-end <b>Retrieval-Augmented Generation (RAG)</b> pipelines with advanced context management, chunking strategies, and hybrid search capabilities.</li><li>​Integrate and tune vector databases (e.g., Milvus, Qdrant, PGVector) to support high-context RAG operations.</li><li>​Establish reproducible pipelines for model <b>fine-tuning</b> (PEFT, LoRA, QLoRA) on private datasets, optimizing memory efficiency and training times.</li></ul><p><b>​3. Agentic AI &amp; Autonomic Systems</b></p><ul><li>​Design and deploy scalable <b>Agentic AI frameworks</b> (e.g., CrewAI, AutoGen, LangGraph) capable of executing multi-step complex workflows, tool use, and self-correction.</li><li>​Develop custom agent &quot;skills&quot;, API integrations, and secure sandboxed execution environments for autonomous agents.</li></ul><p><b>​Required Technical Skills</b></p><p><b>​Core AI &amp; MLOps Stack</b></p><ul><li>​<b>Inference Engines:</b> vLLM (including tensor-parallel/pipeline-parallel setups), Ollama, Hugging Face TGI.</li><li>​<b>Distributed Computing:</b> Ray (Ray Train, Ray Serve, Ray Core).</li><li>​<b>Frameworks &amp; UIs:</b> Open WebUI, LangChain, LangGraph, CrewAI, AutoGen.</li><li>​<b>Data &amp; RAG:</b> Advanced RAG architecture, semantic chunking, metadata filtering, and vector database administration.</li><li>​<b>Fine-Tuning:</b> DeepSpeed, Axolotl, or standard PyTorch-based fine-tuning workflows utilizing LoRA/QLoRA.</li></ul><p><b>​Infrastructure &amp; System Engineering</b></p><ul><li>​<b>Orchestration:</b> Advanced Kubernetes administration, Red Hat OpenShift, Helm charting, and K8s operators (specifically NVIDIA GPU Operator).</li><li>​<b>Operating Systems:</b> Expert-level administration of Linux distributions (<b>Rocky Linux / RHEL</b>, <b>Ubuntu Server</b>).</li><li>​<b>Hardware &amp; Networking:</b> Deep understanding of bare-metal GPU nodes (NVIDIA H100, L40S, A100), PCIe topologies, NVLink, and high-speed networking (InfiniBand/RoCE, SR-IOV).</li><li>​<b>Containers:</b> Docker, Podman, and secure container runtimes (CRI-O, containerd).</li></ul><p>Pay: ₹15,000.00 - ₹40,000.00 per month</p><p>Benefits:</p><ul><li>Provident Fund</li></ul><p>Work Location: In person</p>

Lenovo

<div><div><div><div><h3 class="jobSectionHeader"><b>General Information
</b></h3></div></div><div><div><div><div>Req #
</div><div>WD00100762
</div></div><div><div>Career area:
</div><div>Hardware Engineering
</div></div><div><div>Country/Region:
</div><div>United Kingdom
</div></div><div><div>City:
</div><div>Edinburgh
</div></div><div><div>Date:
</div><div>Thursday, June 11, 2026
</div></div><div><div>Working time:
</div><div>Full-time
</div></div><div><div><b>Additional Locations</b>:
<ul><li>United Kingdom</li></ul>
</div></div></div></div></div><div><div><div><h3 class="jobSectionHeader"><b>Why Work at Lenovo
</b></h3></div></div><div><div><div><div><div><div>We are Lenovo. We do what we say. We own what we do. We WOW our customers.</div><div></div><div><br>
Lenovo is a US$83 billion revenue global technology powerhouse, ranked #196 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world&rsquo;s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo&rsquo;s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).</div></div><br>
<div></div><br>
<div>This transformation together with Lenovo&rsquo;s world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. To find out more visit www.lenovo.com, and read about the latest news via our StoryHub.</div>
</div></div></div></div></div><div><div><div><h3 class="jobSectionHeader"><b>Description and Requirements
</b></h3></div></div><div><div><div><div><div><div><p>Lenovo is seeking an accomplished Advisory AI Infrastructure Engineer to take a leadership role within our Advanced AI Technology Center. In this position, you will architect, build, and evolve the large-scale infrastructure and platforms that underpin AI model development, deployment, and operation across the organization. You will serve as a technical authority on infrastructure strategy, mentor junior engineers, drive best practices, and collaborate cross-functionally with research, engineering, and product teams. Your deep expertise will be instrumental in scaling our AI capabilities and ensuring production-grade reliability, performance, and security. If you are passionate about making Smarter Technology For All, come help us realize our Hybrid AI vision!</p></div><div></div><div><p><b><br>
Responsibilities</b></p></div><div><ul><li>
AI Infrastructure Strategy and Architecture: Define and drive the long-term infrastructure strategy for AI workloads. Architect scalable, cost-efficient, and resilient compute, storage, and networking solutions that support the full AI lifecycle from experimentation through production.</li></ul></div><div><ul><li>
Technical Leadership and Mentorship: Serve as a technical lead and mentor to infrastructure engineers, establishing engineering standards, conducting design reviews, and fostering a culture of operational excellence across the team.</li></ul></div><div><ul><li>
AI Model Deployment and Platform Engineering: Design and own the platforms, frameworks, and processes for deploying, monitoring, and managing AI models at scale in production environments, including model serving, A/B testing infrastructure, and rollback mechanisms.</li></ul></div><div><ul><li>
Advanced Automation and CI/CD: Architect and maintain sophisticated automation pipelines for AI model training, evaluation, testing, and deployment. Champion infrastructure-as-code practices and drive continuous improvement of CI/CD workflows.</li></ul></div><div><ul><li>
Cross-Functional Collaboration: Partner with data scientists, ML engineers, product managers, and leadership to align infrastructure capabilities with research and business objectives. Act as the primary infrastructure point of contact for cross-team initiatives.</li></ul></div><div><ul><li>
Performance Engineering: Lead efforts to profile, benchmark, and optimize AI infrastructure and model serving for throughput, latency, GPU utilization, scalability, and cost efficiency at scale.</li></ul></div><div><ul><li>
Security, Compliance, and Governance: Establish and enforce security best practices, access controls, and compliance frameworks across AI infrastructure. Ensure adherence to relevant regulatory requirements and internal governance policies.</li></ul></div><div><ul><li>
Capacity Planning and Cost Management: Own capacity forecasting, resource planning, and cost optimization for GPU clusters and associated infrastructure, balancing performance needs with budget constraints.</li></ul></div><div></div><div><p><b><br>
Qualifications</b></p></div><div><ul><li>
Bachelor&rsquo;s or Master&rsquo;s degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field. Advanced degree preferred.</li></ul></div><div><ul><li>
8+ years of experience in software engineering, infrastructure engineering, DevOps/SRE, or a related field, with at least 4 years focused on AI/ML infrastructure.</li></ul></div><div><ul><li>
Demonstrated experience leading or mentoring engineering teams on infrastructure projects.</li></ul></div></div><div><div><ul><li>
Deep expertise in computer systems, distributed systems, and cloud computing architectures.</li></ul></div><div><ul><li>
Extensive experience designing, deploying, and managing multi-node distributed GPU clusters using Slurm, Kubernetes, or equivalent orchestration platforms.</li></ul></div><div><ul><li>
Expert-level Linux system administration, including package management, user/group management, file system internals, shell scripting (e.g., Bash), networking, and system configuration (e.g., systemd, kernel tuning).</li></ul></div><div><ul><li>
Strong proficiency in Python and at least one additional systems language (e.g., Go, C++, Rust, Java).</li></ul></div><div><ul><li>
Deep experience with AI-specific hardware and software stacks (e.g., NVIDIA GPUs, CUDA, cuDNN, NCCL, InfiniBand/RoCE networking).</li></ul></div><div><ul><li>
Proven track record managing high-performance computing (HPC) environments, including job scheduling, resource allocation, cluster maintenance, and performance tuning.</li></ul></div><div><ul><li>
Significant experience with AI model deployment, serving infrastructure, and lifecycle management in production.</li></ul></div><div><ul><li>
Strong architectural thinking with the ability to balance trade-offs across performance, reliability, security, and cost.</li></ul></div><div><ul><li>
Excellent communication and collaboration skills, with the ability to influence technical decisions across teams and present to senior leadership.</li></ul></div><div><ul><li>
Ability to thrive in a fast-paced, ambiguous environment and drive clarity through technical leadership.</li></ul></div><div></div><div><p><b><br>
Bonus Points</b></p></div><div><ul><li>
Experience with AI and machine learning frameworks at scale (e.g., PyTorch, DeepSpeed, Megatron-LM, vLLM).</li></ul></div><div><ul><li>
Hands-on experience with major cloud platforms (e.g., AWS, GCP, Azure) and hybrid cloud architectures.</li></ul></div><div><ul><li>
Advanced experience with containerization (Docker) and orchestration (Kubernetes), including custom operators, Helm charts, and GPU scheduling plugins.</li></ul></div><div><ul><li>
Expertise with observability stacks (e.g., Prometheus, Grafana, ELK/OpenSearch, Datadog) for infrastructure and model monitoring.</li></ul></div><div><ul><li>
Experience with infrastructure-as-code tools (e.g., Terraform, Ansible, Pulumi).</li></ul></div><div><ul><li>
Contributions to open-source infrastructure or ML tooling projects.</li></ul></div><div><ul><li>
Experience with large language model (LLM) training and inference infrastructure.</li></ul></div></div><p>
#LATC</p>
</div></div><div><div><div><i>We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class.</i></div>
</div></div></div></div></div><div></div><div><div><div><div><div><b>Additional Locations</b>:
<ul><li>United Kingdom</li></ul>
</div></div><div><ul><li>United Kingdom<br>
</li></ul></div><div></div><div><div><div><br>
<br>
<div><b>AI PROCESSING NOTICE</b></div><div></div><div><br>
We use AI-based tools to support some of our processes (e.g. online interviews recordings and transcripts) in order to achieve better efficiency, accuracy and for our documentation purposes. AI can make mistakes, but we always make sure that the outputs are manually reviewed by a human. You can always opt-out or contact us in case of any question.</div></div></div></div></div></div></div></div>

Advisory AI Infrastructure Engineer

VaporVM

<div><div><b>Date Posted:</b> 11 June, 2026
</div><div><b>Industry:</b> IT Services and IT Consulting
</div><div><b>Location:</b> VaporVM</div></div>
<br><div><h2 class="jobSectionHeader"><b>Job Description:</b></h2>
<div><h1 class="jobSectionHeader"><b>Senior HPC / Infrastructure Engineer</b></h1>
<p><b>Location:</b> Riyadh, Saudi Arabia</p>
<p><b>Employment Type:</b> Full-Time</p>
<p><b>Experience:</b> 10+ Years (Hands-On)</p>
<h2 class="jobSectionHeader"><b>Position Overview</b></h2>
<p>We are seeking a highly experienced <b>Senior HPC / Infrastructure Engineer</b> with proven expertise in designing, deploying, and operating enterprise-scale High-Performance Computing (HPC) and AI infrastructure environments. This role is ideal for a hands-on technical leader who has built and managed production-grade HPC platforms, GPU clusters, Kubernetes ecosystems, and AI infrastructure from the ground up.</p>
<p>The successful candidate will play a critical role in architecting, optimizing, and maintaining mission-critical compute environments that support advanced AI/ML, data science, and high-performance workloads.</p>
<h2 class="jobSectionHeader"><b>Required Certifications</b></h2>
<ul><li><b>RHCE &ndash; Red Hat Certified Engineer (Active)</b></li>
<li><b>CKA &ndash; Certified Kubernetes Administrator (Active)</b></li>
</ul><h2 class="jobSectionHeader"><b>Core Technical Expertise</b></h2>
<h3 class="jobSectionHeader"><b>HPC &amp; NVIDIA AI Ecosystem</b></h3>
<ul><li>NVIDIA Base Command Manager (BCM)</li>
<li>NVIDIA AI Enterprise</li>
<li>NVIDIA GPU Operator &amp; Network Operator</li>
<li>NVIDIA NIM Inference Services</li>
<li>NVIDIA AI Blueprints</li>
<li>CUDA, GPU Drivers, and Performance Optimization</li>
</ul><h3 class="jobSectionHeader"><b>Compute &amp; Container Platforms</b></h3>
<ul><li>Kubernetes (Architecture, Operations &amp; Scaling)</li>
<li>Slurm Workload Manager</li>
<li>Distributed Computing Environments</li>
</ul><h3 class="jobSectionHeader"><b>Operating Systems</b></h3>
<ul><li>Red Hat Enterprise Linux (RHEL)</li>
<li>Ubuntu LTS (Canonical)</li>
</ul><h3 class="jobSectionHeader"><b>Automation &amp; DevOps</b></h3>
<ul><li>CI/CD Pipeline Design &amp; Implementation</li>
<li>Infrastructure Automation</li>
<li>Platform Lifecycle Management</li>
<li>Configuration Management &amp; Orchestration</li>
</ul><h2 class="jobSectionHeader"><b>Key Responsibilities</b></h2>
<ul><li>Design, deploy, and operate large-scale HPC and AI infrastructure environments from bare metal through workload orchestration.</li>
<li>Architect and manage NVIDIA GPU platforms, including BCM, AI Enterprise, GPU Operator, and AI service enablement.</li>
<li>Configure, optimize, and maintain Slurm scheduling environments for high-throughput and GPU-intensive workloads.</li>
<li>Design and operate highly available Kubernetes clusters supporting AI/ML, analytics, and containerized workloads.</li>
<li>Enable and support NVIDIA NIM services and AI Blueprint deployments for enterprise AI initiatives.</li>
<li>Administer and optimize RHEL and Ubuntu environments, ensuring stability, security, and performance.</li>
<li>Develop and maintain infrastructure automation frameworks and CI/CD pipelines for platform and application deployment.</li>
<li>Optimize performance across compute, GPU, storage, networking, and cluster resources.</li>
<li>Implement monitoring, observability, alerting, capacity planning, and operational best practices.</li>
<li>Enforce security, patch management, access controls, and compliance standards across the infrastructure stack.</li>
<li>Lead troubleshooting, root cause analysis, and resolution of complex infrastructure and platform issues.</li>
</ul><h2 class="jobSectionHeader"><b>Candidate Profile</b></h2>
<ul><li>10+ years of hands-on experience in HPC, Linux infrastructure, and enterprise platform engineering.</li>
<li>Proven track record of building and operating production-scale HPC, GPU, or AI infrastructure environments.</li>
<li>Deep expertise in Kubernetes, Slurm, Linux administration, and NVIDIA AI technologies.</li>
<li>Strong understanding of distributed systems, workload scheduling, cluster management, and performance optimization.</li>
<li>Experience supporting AI/ML, data science, and high-performance computing workloads at scale.</li>
<li>Strong analytical, troubleshooting, and problem-solving skills.</li>
<li>Ability to work across infrastructure, platform, automation, and AI enablement domains.</li>
<li>Demonstrated ownership mindset with a history of delivering reliable, scalable, and high-performing solutions.</li></ul></div></div><p></p>

Senior Infrastructure / HPC

Zemin Cosmic Construction and Business Support

<p><b>Senior System Engineer (AI Infrastructure) &ndash; Short-Term Project (3&ndash;5 Months)</b></p><p><b>Job Responsibilities:</b></p><ul><li>Install, configure, and validate 5 physical servers and 16 virtual servers.</li><li>Deploy and manage Ubuntu Server 24.04 LTS and Rocky Linux 9.x environments.</li><li>Configure virtualization platforms, networking, storage, and system services.</li><li>Implement system security hardening, monitoring, and performance optimization.</li><li>Troubleshoot infrastructure, server, and network-related issues.</li><li>Ensure successful setup and readiness of the AI platform infrastructure.</li></ul><p><b>Requirements:</b></p><ul><li>Bachelor's Degree in Computer Science, IT, or a related field.</li><li>Proven experience in Linux system administration (Ubuntu and Rocky Linux/RHEL).</li><li>Strong knowledge of server provisioning, virtualization, networking, and storage technologies.</li><li>Experience with infrastructure deployment and system security best practices.</li><li>Ability to work independently and deliver project milestones on time.</li><li>Must be locally available in Qatar with a valid QID.</li><li>Available to join on short notice/immediately.</li></ul><p><b>Project Duration:</b> 3&ndash;5 Months<br/><b>Location:</b> Qatar<br/><b>Preference:</b> Immediate Joiners Available in Qatar</p><p>Pay: QAR7,000.00 - QAR9,000.00 per month</p><p>Work Location: In person</p>

Senior System Engineer (AI Infrastructure)

Cisco

What You’ll Do
<br><br>
We are seeking a Solutions Engineer – Cloud + AI Infrastructure to join our sales team in the UK In this role you will help drive the adoption of Cisco's data center, cloud, and AI-ready infrastructure solutions across industries. You will partner with customers to understand their modernization goals, support the design of tailored platform architectures, and demonstrate how Cisco's compute, networking, and cloud-native technologies accelerate their business. You'll bring strong infrastructure foundations and a drive to become a trusted technical advisor to customers. You should be comfortable translating technical concepts for diverse audiences and public speaking.
<br><br>
Who You’ll Work With
<br><br>
The Cloud + AI Infrastructure team delivers one scalable strategy with local execution for data center transformation and growth. We are the worldwide go-to-market compute and data center networking engine — capturing market transitions and enabling sellers to expand growth for customers and Cisco. Alongside our colleagues, we build the sales strategy, activate sellers and technical communities, and accelerate selling.
<br><br>
Who You Are
<br><br>
You build awareness, foster education, and drive energy for platform modernization, cloud-native infrastructure, and AI-ready data centers with internal teams, partners, and customers. You act as the link between technical teams, account teams, and partners/customers. You showcase infrastructure solutions, articulate their value, and contribute to architectural discussions. You play a key role in the design, and promotion of modernization solutions and in solving customer challenges. Travel may be required for customer meetings and industry events.
<br><br>
<b>Minimum Qualifications:</b><br>
<ul>
<li>3+ years of technical presales, solutions engineering, or relevant hands-on infrastructure experience across compute, storage, and data center networking.</li>
<li>Hands-on experience with virtualization and platform modernization (e.g., VMware/VCF, RedHat OpenShift, Nutanix, KVM, hypervisor migration strategies).</li>
<li>Familiarity with enterprise storage concepts and platforms (e.g., NVMe, NFS, object storage; NetApp, Everpure, or comparable.</li>
<li>Solid understanding of data center networking fundamentals (L2/L3, VXLAN/EVPN, fabric design) and how compute, storage, and network integrate.</li>
<li>Foundational understanding of Kubernetes and cloud-native infrastructure — core concepts, deployment models, and how containerized workloads are managed.</li>
<li>Proficiency with infrastructure automation and scripting (e.g., Python, Ansible, Terraform) and version control (Git/GitHub).</li>
</ul>
<b>Preferred Qualifications:</b><br>
<ul>
<li>Bachelor’s Degree in Computer Science, Computer Engineering, Electrical Engineering, or related field; advanced degree is a plus.</li>
<li>Excellent presentation skills with the ability to deliver engaging workshops to both technical and non-technical audiences on infrastructure and modernization topics.</li>
<li>Experience with the Cisco data center portfolio (UCS, Nexus/ACI, Intersight, HyperFabric) or comparable competitive platforms.</li>
<li>Exposure to cloud-native networking and eBPF technologies (e.g., Cilium/Isovalent), GitOps workflows (ArgoCD/Flux), Helm, and service mesh.</li>
<li>Familiarity with AI-ready infrastructure: GPU compute, high-performance fabrics (RoCE/InfiniBand), AI/ML reference architectures (e.g., Cisco + NVIDIA), and storage for AI workloads.</li>
<li>Relevant industry certifications (e.g., CCNP/CCIE Data Center, VMware VCP, CKA/CKAD, or major cloud associate/professional certs)</li>
<li>Familiarity with data center observability and monitoring tooling (e.g., Prometheus, Grafana, ThousandEyes, Splunk).</li>
<li>Understanding of infrastructure security and zero-trust principles in data center / cloud-native environments (segmentation, workload identity, policy enforcement).</li>
</ul>
Why Cisco?
<br><br>
At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint.
<br>Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere.
<br>We are Cisco, and our power starts with you.

Solutions Engineer - Cloud & AI Infrastructure

Recro

<div>
 <p>
  Core Must-Have Requirements (Non-Negotiable)
 </p>
 <ul>
  <li>
   <strong>
    Agentic AI Experience
   </strong>
   Built AI agents in production.
  </li>
  <li>
   Experience with memory systems, RAG, retrieval pipelines, tool calling, workflow orchestration.
  </li>
  <li>
   Worked with frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, etc.
  </li>
  <li>
   Experience designing multi-step agent workflows rather than simple chatbot integrations.
  </li>
  <li>
   <strong>
    Self-Hosted Model Deployment
   </strong>
   Hands-on experience deploying and serving open-source models.
  </li>
  <li>
   Experience with:
  </li>
  <li>
   vLLM
  </li>
  <li>
   Triton Inference Server
  </li>
  <li>
   Ollama (less preferred)
  </li>
  <li>
   TGI (Text Generation Inference)
  </li>
  <li>
   Strong understanding of GPU utilization, batching, latency optimization, throughput, and inference costs.
  </li>
  <li>
   <strong>
    LLM Gateway &amp; Orchestration
   </strong>
   Experience managing multiple model providers and routing strategies.
  </li>
  <li>
   Exposure to:
  </li>
  <li>
   LiteLLM
  </li>
  <li>
   Portkey
  </li>
  <li>
   OpenRouter
  </li>
  <li>
   Custom LLM gateways
  </li>
  <li>
   Model selection, fallback logic, observability, rate limiting, and cost controls.
  </li>
 </ul>
 <p>
  Strongly Preferred
 </p>
 <ul>
  <li>
   <strong>
    Production AI Infrastructure
   </strong>
   Kubernetes
  </li>
  <li>
   Docker
  </li>
  <li>
   GCP (preferred) or AWS
  </li>
  <li>
   CI/CD for ML systems
  </li>
  <li>
   Monitoring and alerting
  </li>
  <li>
   <strong>
    Evaluation &amp; Observability
   </strong>
   Building evaluation frameworks
  </li>
  <li>
   A/B testing models
  </li>
  <li>
   Tracking metrics such as:
  </li>
  <li>
   WER (speech)
  </li>
  <li>
   DER (diarization)
  </li>
  <li>
   Recall/F1 (retrieval)
  </li>
  <li>
   Latency
  </li>
  <li>
   Cost per request
  </li>
  <li>
   Experience with Langfuse, Arize, Weights &amp; Biases, OpenTelemetry, Grafana, Prometheus, etc.
  </li>
 </ul>
 <p>
  Good-to-Have
 </p>
 <ul>
  <li>
   <strong>
    Speech/Audio AI
   </strong>
   Whisper, Deepgram, AssemblyAI, Speechmatics
  </li>
  <li>
   Speaker diarization
  </li>
  <li>
   Voice identification
  </li>
  <li>
   Real-time audio pipelines
  </li>
  <li>
   <strong>
    Research Background
   </strong>
   Publications
  </li>
  <li>
   Applied ML research
  </li>
  <li>
   Fine-tuning models
  </li>
 </ul>
 <p>
  Ideal Candidate Background
 </p>
 <ul>
  <li>
   3&ndash;5 years of AI/ML Engineering experience.
  </li>
  <li>
   Strong Python engineer.
  </li>
  <li>
   Has built and shipped agentic AI systems.
  </li>
  <li>
   Has deployed and optimized self-hosted LLMs.
  </li>
  <li>
   Understands model serving, observability, evaluation, and infrastructure.
  </li>
  <li>
   Audio/STT experience is a bonus, not mandatory.
  </li>
 </ul>
 <p>
  <br/>
 </p>
</div>


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