ai infrastructure engineer

Dautom

<div>
 
 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.
 
 
 
 
 
 Key Responsibilities &amp; Deliverables
 
 
 
 
 
 GPU &amp; AI Platform Architecture
 
 
 Design and implement GPU-based compute clusters. Define reference architectures for LLM hosting, Vector Databases, MLOps, and high-performance storage/networking.
 
 
 Fully operational GPU-based AI infrastructure. GPU Cluster Uptime and Performance Utilization. Reduction in Cost per Training/Inference Workload.
 
 
 GPU Cluster Operations
 
 
 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).
 
 
 High-availability architecture for all AI workloads. Complete documentation and runbooks.
 
 
 OpenShift AI (RHODS) Management
 
 
 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.
 
 
 Production-ready OpenShift AI (RHODS) platform. AI Project Onboarding Speed.
 
 
 LLM &amp; Model Serving
 
 
 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).
 
 
 Multi-model LLM serving environment for entities. MLOps Pipeline Success Rate and Deployment Frequency.
 
 
 MLOps &amp; Automation
 
 
 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).
 
 
 Infrastructure automation via Terraform &amp; Ansible. Automation Coverage for AI Infrastructure.
 
 
 
 
 
 Required Qualifications &amp; Experience
 
 <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>
 
 Essential Skills &amp; Competencies
 
 <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>
 
 Preferred Certifications
 
 <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>
 
 
 
</div>

AI Infrastructure Engineer

RCV Innovations Pvt Ltd

Job Description ​We are seeking a highly skilled and hands-on AI Infrastructure Engineer 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.​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.​Key Responsibilities​1. AI Infrastructure &amp; Deployment Orchestration<ul><li>​Design, provision, and manage high-performance compute clusters on bare-metal (Rocky Linux / Ubuntu) and enterprise platforms (Red Hat OpenShift / Kubernetes).</li><li>​Deploy, optimize, and maintain scalable LLM/VLM serving engines using vLLM and Ollama, ensuring low-latency and high-throughput inference.</li><li>​Configure and scale distributed training and inference workloads using Ray framework, specifically optimizing for multi-GPU, multi-modal applications (Text, Image, and Audio).</li><li>​Set up, secure, and customize enterprise-grade user interfaces like Open WebUI for cross-functional team access.</li></ul>​2. Advanced RAG &amp; Model Lifecycle Management<ul><li>​Architect and maintain end-to-end Retrieval-Augmented Generation (RAG) 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 fine-tuning (PEFT, LoRA, QLoRA) on private datasets, optimizing memory efficiency and training times.</li></ul>​3. Agentic AI &amp; Autonomic Systems<ul><li>​Design and deploy scalable Agentic AI frameworks (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>​Required Technical Skills​Core AI &amp; MLOps Stack<ul><li>​Inference Engines: vLLM (including tensor-parallel/pipeline-parallel setups), Ollama, Hugging Face TGI.</li><li>​Distributed Computing: Ray (Ray Train, Ray Serve, Ray Core).</li><li>​Frameworks &amp; UIs: Open WebUI, LangChain, LangGraph, CrewAI, AutoGen.</li><li>​Data &amp; RAG: Advanced RAG architecture, semantic chunking, metadata filtering, and vector database administration.</li><li>​Fine-Tuning: DeepSpeed, Axolotl, or standard PyTorch-based fine-tuning workflows utilizing LoRA/QLoRA.</li></ul>​Infrastructure &amp; System Engineering<ul><li>​Orchestration: Advanced Kubernetes administration, Red Hat OpenShift, Helm charting, and K8s operators (specifically NVIDIA GPU Operator).</li><li>​Operating Systems: Expert-level administration of Linux distributions (Rocky Linux / RHEL, Ubuntu Server).</li><li>​Hardware &amp; Networking: Deep understanding of bare-metal GPU nodes (NVIDIA H100, L40S, A100), PCIe topologies, NVLink, and high-speed networking (InfiniBand/RoCE, SR-IOV).</li><li>​Containers: Docker, Podman, and secure container runtimes (CRI-O, containerd).</li></ul>Pay: ₹15,000.00 - ₹40,000.00 per monthBenefits:<ul><li>Provident Fund</li></ul>Work Location: In person

Google

<div><h3 class="jobSectionHeader">Minimum qualifications:</h3><ul>
<li>Bachelor's degree in Computer Science or equivalent practical experience.</li>
<li>3 years of experience building machine learning solutions and working with technical customers.</li>
<li>Experience designing cloud enterprise solutions and supporting customer projects to completion.</li>
<li>Experience coding in one or more general purpose languages (e.g., Python, Java, Go, C or C++) including data structures, algorithms, and software design.</li></ul> 
<h3 class="jobSectionHeader">
Preferred qualifications:</h3><ul>
<li>Experience working with recommendation engines, data pipelines, or distributed machine learning.</li>
<li>Experience with deep learning frameworks (e.g., TensorFlow, XGBoost).</li>
<li>Understanding of the auxiliary practical concerns in production machine learning systems.</li>
<li>Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools and environments (e.g., Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce).</li>
</ul><div><h2 class="jobSectionHeader">About the job</h2>
The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google&rsquo;s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners. 

As a Cloud AI Engineer, you will design and implement machine learning solutions for customer use cases, leveraging core Google products including TensorFlow, DataFlow, and Vertex AI. You will work with customers to identify opportunities to apply machine learning in their business, and travel to customer sites to deploy solutions and deliver workshops to educate and empower customers. You will work closely with product management and product engineering to build and constantly drive excellence in our products. 
 
In this role, you are the Google engineer working with Google's largest and most motivated Cloud customers. Together with the team you will support customer implementation of Google Cloud products through architecture guidance, best practices, data migration, capacity planning, implementation, troubleshooting, monitoring, and much more. 
Google Cloud accelerates every organization&rsquo;s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google&rsquo;s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.</div><div><h2 class="jobSectionHeader">
Responsibilities</h2><ul>
<li>Be a trusted technical advisor to customers and solve complex machine learning challenges.</li>
<li>Coach customers on the practical challenges in machine learning systems feature extraction and feature definition, data validation, monitoring, and management of features and models.</li>
<li>Work with customers, partners, and Google Product teams to deliver tailored solutions into production.</li>
<li>Create and deliver best practice recommendations, tutorials, blog articles, and sample code.</li>
<li>Travel up to 30% for in-region for meetings, technical reviews, and onsite delivery activities.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

BRAIN

<div><h2 class="jobSectionHeader">About Brain Co.</h2>
Brain Co. is an applied AI startup co-founded by Jared Kushner and Elad Gil, and backed by leading Silicon Valley builders including Patrick Collison and Andrej Karpathy. 
 
We are building AI applications for the world&rsquo;s most important institutions, delivering impact on real-world problems across governments, healthcare systems, and critical industries. 
 
Our progress so far:<ul><li>
Automated construction permitting for a sovereign government 80% faster, unlocking $375M+ in value</li><li>
Optimized supply chains for a leading global energy company 30% lower cost, 99% reliability, preventing $100M+ in losses</li><li>
Streamlined hospital patient care across national health systems 40% better outcomes, 80% less admin work</li></ul>
Company momentum:<ul><li>
Raised a $55M Series A from leading investors</li><li>
Built a team of 70+ AI experts from Tesla, Google DeepMind, NVIDIA, and Databricks</li></ul>
At Brain Co., we focus on applying frontier AI to real institutional challenges, working alongside governments, healthcare systems, and critical industries to modernize how essential services operate. 
 
We are looking for leaders who want to help bring new technology into institutions that impact millions of people.<div> 
</div><h2 class="jobSectionHeader">
What You&rsquo;ll Work On:</h2><ul><li>
Design and scale Kubernetes- and Terraform-based infrastructure across customer environments.</li><li>
Define standards for networking, security, CI/CD, and multi-region deployments.</li><li>
Build and maintain metrics, logging, tracing, dashboards, and SLOs.</li><li>
Diagnose and improve performance across distributed systems and AI workloads.</li><li>
Support high-performance inference, data pipelines, and large-scale backend services.</li><li>
Ensure systems scale reliably under fast-growing and unpredictable workloads.</li><li>
Partner with technical leaders on architecture and mentor engineers.</li></ul><h2 class="jobSectionHeader">
You Might Be a Great Fit If You&hellip;</h2><ul><li>
Have 5+ years building large-scale backend or infra systems.</li><li>
Know Kubernetes, Terraform, cloud networking, orchestration, and observability tools.</li><li>
Bring strong distributed systems, performance, and incident-response experience</li><li>
Work well with customers and teams to deliver reliable solutions.</li><li>
Thrive in high-agency environments and set engineering standards.</li><li>
Enjoy ambiguity, autonomy, and solving tough infra problems.</li></ul><h2 class="jobSectionHeader">
Why Join Us</h2><ul><li>
Ship quickly, iterate constantly and see your work deployed at global scale</li><li>
Collaborate with industry veterans from Tesla, DeepMind, Databricks, and more</li><li>
Accelerate your career with ownership based on impact, not tenure</li><li>
Earn competitive compensation + meaningful equity in a high-growth company</li><li>
Thrive in a culture built on speed, curiosity, and impact</li></ul><h2 class="jobSectionHeader">
Benefits</h2><ul><li>
Competitive salary plus equity</li><li>
Daily lunches</li><li>
Commuter benefits</li><li>
Unlimited PTO</li></ul></div>

AI Platform & Infrastructure Engineer, Deployed

Accenture

<div>YOU ARE
As a hands-on Infrastructure architect, you are an early-career engineer who learns and grows while contributing hands-on to the AI and machine learning infrastructure that powers real-world applications. Under the guidance of senior architects and engineers, you'll develop practical skills in coding, testing, configuring, deploying, monitoring, and troubleshooting AI systems and the infrastructure they run on. Day to day, you'll write and test code and deployment scripts, help configure cloud and on-premises compute resources such as GPU clusters and distributed training environments, deploy AI systems and models into production, and support data pipelines that feed AI and ML workflows. You'll learn to monitor AI systems and infrastructure health across both InfraOps and MLOps disciplines, perform AI monitoring to track model and system performance, and troubleshoot issues across the computational stack with mentorship and support. This is a hands-on, learning-focused where you build expertise across modern tools and platforms &mdash; including container orchestration, model serving, CI/CD pipelines, InfraOps, MLOps, and AI monitoring &mdash; while making meaningful contributions to infrastructure that enables AI-driven busin
ess outcomes.
THE WORK<ul><li>
Write, test, and debug code and scripts for AI infrastructure tasks, including automation and tooling, under the guidance of senior engineers.</li><li>
Develop and maintain infrastructure and software deployment scripts to support reliable, repeatable releases of AI systems and models.</li><li>
Configure and provision compute resources across cloud and on-premises environments, including GPU clusters and distributed training setups.</li><li>
Deploy AI systems and machine learning models into production infrastructure, following established processes and best practices.</li><li>
Deploy data pipelines that feed AI and ML workflows, ensuring data is available, clean, and reliable.</li><li>
Assist with container orchestration and model serving, learning tools such as Docker, Kubernetes, and model deployment frameworks.</li><li>
Support and maintain CI/CD pipelines for automating the build, test, and deployment of AI infrastructure and applications.</li><li>
Monitor AI systems and infrastructure health across InfraOps and MLOps disciplines, tracking performance, reliability, and resource utilization.</li><li>
Perform AI monitoring to track model performance, detect drift or degradation, and surface issues for review.</li><li>
Troubleshoot and help resolve issues across the computational stack &mdash; hardware, networking, software, and models &mdash; with mentorship and support.</li><li>
Document configurations, processes, and procedures to maintain clear, repeatable, and shareable knowledge across the team.</li><li>
Collaborate with senior architects and engineers, participating in code reviews, team discussions, and knowledge-sharing sessions to grow technical skills.</li><li>
Apply security, cost-efficiency, and scalability best practices as you learn them, contributing to well-managed and responsible infrastructure.</li></ul></div>
 
<div>EDUCATION<ul><li>
Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field</li></ul>
BASIC (REQUIRED) QUALIFICATION<ul><li>
Work or coursework experience with AI/ML or Computer engineering or Computer science.</li><li>
Coding, building, monitoring, troubleshooting applications of AI/ML models; selecting, designing and infrastructure for deploying and running them on premise or on public cloud.</li><li>
Strong understanding of AI and machine learning as a subject.</li><li>
Strong understanding of computing infrastructure a subject, preferred knowledge of AI infrastructure.</li><li>
Proficiency in programming languages such as Python, Java, or C++.</li></ul></div>

AI Infrastructure Architecture Analyst

Smvsoft LLC

Founding Engineer - Applied AI San francisco, CA (Full time)- In-Office$180k-$230k per annumNO C2CAbout the RoleHiring a world-class Applied AI Engineer to build the core systems behind their automation engine for hardware engineering.This is a deeply technical, high-impact role. You will be among the first technical hires and will own mission-critical systems end-to-end. The problems are ambiguous, the constraints are real, and the expectations are high. If you move fast from idea to production, care deeply about customer experience, and want to build foundational AI infrastructure for the world's most impactful industries, this role is for you.Key Responsibilities<ul><li>Design and implement core workflows and infrastructure for hardware engineering agents, including intelligent context management, novel indexing approaches, and post-training</li><li>Build agents and ML models that can interpret, reason over, and manipulate complex engineering design data, integrating with enterprise-grade engineering tools and systems</li><li>Work closely with customers and the engineering team to deliver AI-powered workflows that are reliable, intuitive, and trusted in production environments</li><li>Prototype rapidly, validate ideas with real users, and ship frequently</li><li>Influence engineering culture, architecture decisions, and the future of the product</li></ul>RequirementsMust-Have<ul><li>Bachelor's or higher in Computer Science, Machine Learning, or equivalent experience</li><li>Strong familiarity with LLM architectures and agent research trends</li><li>Excellent programming skills with experience using ML libraries such as PyTorch, Hugging Face, and W&amp;B</li><li>Experience building production ML or AI systems</li><li>Solid practical experience with search and information retrieval</li><li>Moves fast from idea to production, with a genuine care for customer experience</li><li>Based in or willing to relocate to San Francisco, or an exceptional remote candidate</li></ul>Nice-to-Have<ul><li>2+ years in applied machine learning, backend engineering, or AI infrastructure</li><li>Experience working with machine learning over geometric data, including CAD</li><li>Track record of high-impact contributions or published papers at top-tier AI labs or teams</li></ul>Pay: $180,000.00 - $230,000.00 per yearWork Location: In person

Founding Engineer - Applied AI

Enpal

<div>
 At Enpal, we are not just a company; we are a movement. As a leading European greentech unicorn, we are on a mission to make clean energy simple, accessible, and scalable for homeowners across the continent.
 
 
 Through solar panels, heat pumps, home batteries, and EV charging, we are redefining residential energy. With Enpal Energy, we are building Europe&rsquo;s leading platform to manage and orchestrate millions of distributed energy assets &mdash; laying the foundation for the largest Virtual Power Plant (VPP) in Europe.
 
 
 Our vision: power Europe&rsquo;s transition to a fully decentralized, intelligent, and clean energy system.
 
 
 
 Job Description
 
 
 
 As a
 
 Senior AI Platform Engineer
 
 , you will design and build the internal AI Engine that powers all AI capabilities across Enpal Energy. This is not about wrapping APIs &mdash; it is about building the core infrastructure that makes AI reliable, scalable, and production-grade.
 
 
 We are looking for a systems thinker with strong engineering fundamentals and a deep understanding of modern AI systems. You care about reliability, observability, and building platforms that enable other engineers to move fast with confidence.
 
 
 What you will do
 
 
 <ul>
 <li>
 Own the design and development of the internal AI platform powering all AI features.
 </li>
 <li>
 Build a robust Agent Runtime to orchestrate complex AI workflows in production.
 </li>
 <li>
 Design scalable backend services and developer-facing APIs for AI systems.
 </li>
 <li>
 Build internal tooling for evaluation, observability, tracing, and debugging (latency, cost, quality, reliability).
 </li>
 <li>
 Define standards for AI quality, including evaluation frameworks, versioning, and reproducibility.
 </li>
 <li>
 Collaborate closely with product and engineering teams to enable fast, safe, and deterministic AI feature delivery.
 </li>
 <li>
 Own CI/CD pipelines, infrastructure as code, and production readiness of AI systems.
 </li>
 <li>
 Continuously improve performance, reliability, and cost efficiency.
 </li>
 <li>
 Contribute to architectural decisions shaping the long-term AI strategy.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 5&ndash;7+ years of software engineering experience (mid to senior level).
 </li>
 <li>
 Strong background in backend systems, platform engineering, or internal developer platforms.
 </li>
 <li>
 Strong proficiency in TypeScript and Node.js (JavaScript ecosystem).
 </li>
 <li>
 Solid understanding of modern AI system design, including agentic architectures vs. chains, tool use and function calling, and orchestration patterns.
 </li>
 <li>
 Experience in LLM APIs (e.g. OpenAI, Anthropic, DeepSeek) and AI infrastructure.
 </li>
 <li>
 Experience building observability, logging, tracing, and evaluation systems.
 </li>
 <li>
 Strong focus on reliability, determinism, and production-grade engineering.
 </li>
 <li>
 Experience building APIs and integrating complex systems.
 </li>
 <li>
 Familiarity with CI/CD tools (GitHub Actions, Azure DevOps, or similar).
 </li>
 <li>
 Strong commitment to clean, testable, and scalable code.
 </li>
 <li>
 (Senior level) Experience mentoring engineers and driving technical decisions.
 
 
 </li>
 </ul>
 Additional Information
 
 
 <ul>
 <li>
 Join Germany&rsquo;s leading green unicorn and help shape the energy transition.
 </li>
 <li>
 Work in a diverse team of 65+ nationalities united by one mission.
 </li>
 <li>
 Hybrid work model: office or remote &mdash; your choice.
 </li>
 <li>
 Our Berlin HQ offers a fully equipped startup environment (rooftop, gym, lounges, events).
 </li>
 <li>
 Structured onboarding, including a welcome session with our founder.
 </li>
 <li>
 Regular company updates, technical deep dives, and knowledge sharing sessions.
 </li>
 <li>
 Strong team culture, ownership mindset, and frequent team events.
 </li>
 <li>
 A strong feedback culture that values continuous improvement.
 
 
 </li>
 </ul>
 At Enpal, diversity and inclusion are core values. Hiring decisions are made without discrimination of any kind.
</div>

AI Platform Engineer (f/m/d)

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5179 ai infrastructure engineer jobs Near Me

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Founding Engineer - Applied AI

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