ai infrastructure engineer

Openkyber

<div><p>We are looking for FullStack AI Engineer for our client in Malvern, PA Job Title: FullStack AI Engineer Job Location: Malvern, PA Job Type: Contract Job Description: Pay Range: $65hr - $70hr Solution Design Implementation Design and implement endtoend generative AI solutions leveraging services like Amazon Bedrock including Agents Knowledge Bases and Guardrails. Model Development Optimization Finetune and customize large language models LLMs and other generative models GANs VAEs for specific business needs ensuring optimal performance and scalability. Prompt Engineering RAG Apply prompt engineering methodologies and build RetrievalAugmented Generation RAG systems to enhance model accuracy reliability and contextual understanding. Build CICD pipelines to automate the model lifecycle including training deployment monitoring and retraining. Cloud Infrastructure IaC Develop and manage robust cloud infrastructure using Infrastructure as Code IaC tools like AWS CloudFormation AWS CDK or Terraform. Collaboration Documentation Work with product managers to integrate AI capabilities into existing systems and create technical documentation best practices and tutorials. Security Compliance Ensure all AI solutions meet AWS best practices for security compliance scalability and cost optimization. Proficiency in Python is essential along with experience in languages like Java or TypeScript. Handson experience with core AWS services including S3 Lambda EC2 DynamoDB API Gateway Step Functions Glue and Athena Strong understanding and experienc in orchestration frameworks like LangChain. Technical Concepts Expertise in Generative AI architectures LLMs vector databases statistical methods and data processing. Strong problemsolving analytical and communication skills for working in crossfunctional teams and interacting with customers.</p>
<p><b>For applications and inquiries, contact:</b> hirings@openkyber.com</p></div>

OCI AI Infrastructure Engineer

COMPLEX

<div><div><b>Company And Culture</b></div>
<div></div>
<div>Complex is the definitive platform for global youth culture and music lifestyle, seamlessly integrating cutting-edge content, commerce and live experiences with unparalleled scale. Through innovative content, Complex tells stories of music, streetwear and style, sports, art and beyond. Its content engages in a dynamic conversation with the audience, reflecting and shaping the zeitgeist of convergence culture. A powerful media juggernaut paired with a curated marketplace, Complex is redefining the way fans interact with their favorite brands and artists and reshaping the future of digital culture and commerce.</div>
<div></div>
<div><b>Why We're Hiring
</b></div>
<div></div><div>Complex operates at the intersection of content, commerce, and culture&mdash;powering media, live experiences, marketplace, and audience engagement across sneakers, music, sports, and streetwear. This ecosystem generates a high volume of workflows, audience intelligence, and creative output. Much of that operational value, however, remains fragmented across SaaS platforms, manual processes, and institutional knowledge. We are building the next layer of infrastructure to change that.
</div>
<div></div><div>The AI Orchestra Director: Agentic Infrastructure Engineer will embed directly within our technology organization, ensuring that every architectural decision drives measurable business outcomes. The role bridges system design, implementation, and operational impact. Complex is seeking an AI Orchestra Director&mdash;a hands-on technical architect and builder responsible for designing, deploying, and operationalizing our agentic AI infrastructure. This is not an advisory position. It is an execution-driven engineering mandate to build a parallel infrastructure layer that enables AI agents to:</div>
<ul><li>Automate core workflows</li></ul>
<ul><li>Replace high-cost SaaS platforms with owned solutions</li></ul>
<ul><li>Modernize data access and experimentation</li></ul>
<ul><li>Generate proprietary, reusable intellectual property</li></ul>
<ul><li>All while the business continues to operate without disruption</li></ul>
<div></div>
<div><b>Work Schedule:</b></div>
<div>Possible freelance to hire, 4-6 month project Monday - Friday</div>
<h3 class="jobSectionHeader"><b>What You'll Do</b></h3><ul>
<li><b>Top Three Strategic Priorities:</b></li>
<li>1. Architect and Deploy the Agentic Infrastructure Layer</li>
<li>Design and implement a scalable runtime environment purpose-built for a media and commerce organization, including:</li>
<li>Agent orchestration and task routing</li>
<li>Quality gates and human-in-the-loop checkpoints</li>
<li>Logging, observability, and convergence tracking</li>
<li>CI/CD and deployment pipelines</li>
<li>On-premise or hybrid environment architecture</li>
<li>This foundation must be secure, extensible, and maintainable by the existing engineering team</li>
</ul>
<div></div><ul><li>2. Lead the Tool Replacement Program</li>
<li>Audit current SaaS licenses and identify high-cost, high-leverage replacement opportunities (e.g., CRM, analytics, workflow tools)</li>
<li>Using agentic development practices:</li>
<li>Build internal tools with AI agents writing and testing code</li>
<li>Maintain human review at critical checkpoints</li>
<li>Deliver measurable license cost reductions</li>
<li>Demonstrate infrastructure ROI within the engagement period</li>
<li>The objective: prove the system&rsquo;s value by using agents to build the tools themselves</li>
</ul>
<div></div><ul><li>3. Codify Operational Workflows into a Reusable Skills Library</li>
<li>Partner across departments to:</li>
<li>Identify and document high-impact operational workflows</li>
<li>Encode them into version-controlled, reusable agent &ldquo;skills&rdquo;</li>
<li>Transform tribal knowledge into durable company-owned IP</li>
<li>The result: a scalable library of automated capabilities that can operate independently of the engagement</li>
</ul>
<div></div><ul><li><b>Key Responsibilities:</b></li>
<li>Architect and deploy the agentic infrastructure layer in collaboration with executive leadership and the Platform Studio engineering team</li>
<li>Design a modern, growth-oriented data access layer built on existing Snowflake infrastructure and related data sources</li>
<li>Enable agent-native querying, rapid experimentation, and insight generation</li>
<li>Evaluate and integrate open-source orchestration tools to maintain vendor-agnostic flexibility</li>
<li>Implement test-driven development enforcement, intelligent retry logic, and convergence tracking</li>
<li>Establish governance frameworks with human-in-the-loop checkpoints for production deployment and auditability</li>
<li>Embed seamlessly with existing engineering teams&mdash;building trust, not parallel power structures</li>
<li>Develop documentation, training protocols, and knowledge-transfer systems to ensure long-term sustainability</li>
<li>Partner with the COO and department leaders to prioritize automation initiatives that simultaneously reduce cost and increase velocity</li>
<li>Foster a culture shift toward agentic development as a core capability within the organization</li>
</ul><h3 class="jobSectionHeader"><b>Who You Are</b></h3><ul>
<li>Proven experience building and deploying AI agent infrastructure in production environments</li>
<li>Deep proficiency across LLM orchestration frameworks, retrieval-augmented generation (RAG), prompt engineering, and agentic coding workflows</li>
<li>Strong systems architecture background with infrastructure designed for long-term maintainability</li>
<li>Experience with data engineering and analytics infrastructure (Snowflake and API-based systems preferred)</li>
<li>Comfort evaluating and integrating open-source tooling within a multi-model ecosystem (e.g., Claude, Codex, Gemini, open-source models)</li>
<li>Git-native workflow mindset (branches, PRs, CI/CD, TDD)</li>
<li>Exceptional ability to translate complex architecture into business-aligned outcomes</li>
<li>Experience embedding within existing teams and leaving them stronger than you found them</li>
<li>Media, entertainment, or commerce industry experience preferred but not required</li>
<li>Comfortable with whiteboarding systems with executive leadership</li>
<li>Creating shipping production-ready pipelines</li>
<li>Reviewing agent-generated code</li>
<li>Ability to train teams to extend what you&rsquo;ve built</li>
</ul>
<div></div><ul><li><b>Success Metrics (By the end of the 12 week engagement, you will have delivered):</b></li>
<li>A working agent runtime environment with orchestration, logging, quality gates, and CI/CD pipelines</li>
<li>At least two deployed internal SaaS replacements with documented cost savings</li>
<li>A versioned skills library containing a minimum of ten codified operational workflows</li>
<li>A defined and partially implemented data access layer serving both human users and AI agents</li>
<li>Complete knowledge transfer to the existing engineering team</li>
<li>Seamless organizational integration with no structural friction</li>
</ul>
<div></div><ul><li><b>What Success Looks Like:</b></li>
<li>Success is not measured solely by architectural elegance &ndash; It is measured by operational independence</li>
<li>Workflows that execute autonomously</li>
<li>Tools built internally rather than rented</li>
<li>Data systems that surface insights proactively</li>
<li>An engineering organization fluent in agentic development</li>
</ul><div>The Pay Range, which consists of salary and commission, for this position is listed. Actual pay will vary based on factors including, but not limited to location, experience, and performance. The range listed is just one component of Complex Total Rewards offerings for employees.</div><div>
We're an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.</div>
<div></div>
<div><i>The above statements cover what are generally believed to be principal and essential functions of the job. Specific circumstances may allow or require some incumbents assigned to the job to perform a different combination of duties.</i></div>
<div></div>
<div><i>Complex participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.</i></div>
<div></div>
<div><i>Candidates must be legally authorized to work in the U.S. without the need for visa sponsorship.</i></div></div>

Director, Agentic AI Infrastructure & Orchestration Engineer

Value Engineering

<p>Reporting to <b>AI Lead / Solution Architect</b>, this is a <b>senior technical leadership role</b> responsible for:<br/>Designing and deploying <b>state-of-the-art LLMs and multi-modal AI models</b>.<br/>Implementing <b>agentic AI systems with RAG, self-RAG, and memory-optimized architectures</b>.<br/>Leading <b>cloud-based GPU LLM training pipelines</b>, including <b>data labeling, preprocessing, model training, and post-processing</b>.<br/>Extending AI models into <b>front-end applications and back-end pipelines</b>.<br/>Managing <b>staging, development, and production environments</b> with SSH deployment and editing.<br/>Driving <b>complex problem-solving, critical thinking, and innovative AI solutions</b></p><p><b>Key Responsibilities</b></p><p>1. <b>LLM Cloud Training &amp; Pipelines (Mandatory)</b></p><p>Design and deploy <b>LLM training pipelines</b> on cloud GPU infrastructure.<br/>Manage <b>data labeling, preprocessing, and post-processing</b> for training datasets.<br/>Optimize <b>model training, memory usage, and inference efficiency</b>.<br/>Implement <b>RAG-based pipelines, agentic RAG, and self-RAG</b> for knowledge augmentation.</p><p>2. <b>Deep Learning &amp; Model Development</b></p><p>Research, design, and implement <b>Transformers, Vision Transformers, Diffusion Models</b>. Optimize <b>SAM models for tabular and image data</b>, including preprocessing and post-processing.<br/>Implement <b>multi-modal AI pipelines</b> (text, image, video).<br/>Develop <b>agentic AI models</b> with adaptive memory and reasoning.</p><p>3. <b>Agentic AI &amp; Orchestration</b></p><p>Design <b>agent frameworks</b> with orchestration, inter-agent communication, and protocol management.<br/>Integrate <b>LangChain, LangGraph, or custom agent pipelines</b>.<br/>Ensure AI agents are <b>robust, scalable, and production-ready</b>.</p><p>4. <b>Machine Learning for Real-World Data</b></p><p>Implement ML models for <b>tabular, image, and multi-modal data</b>.<br/>Preprocess, normalize, and feature-engineer datasets for <b>SAM and other model types</b>.<br/>Translate research models into <b>production pipelines with RAG augmentation</b>.</p><p>5. <b>Front-End &amp; Back-End AI Integration</b></p><p>Extend AI models into <b>front-end applications</b> (interactive AI features).<br/>Integrate AI models into <b>back-end services</b> for low-latency inference.<br/>Ensure seamless integration between AI models and application architecture.</p><p>6. <b>Production AI Infrastructure &amp; Deployment</b></p><p>Deploy AI models on <b>MCP / GPU servers, distributed clusters, or cloud infrastructure</b>.<br/>Manage <b>staging, dev, and production environments</b>, including SSH deployment, editing, and version control.<br/>Implement monitoring and observability using <b>Prometheus, Grafana, and custom dashboards</b>.<br/>Maintain CI/CD for AI models, training pipelines, and data workflows, including <b>Git/GitHub workflow management</b>.</p><p>7. <b>Research to Production Translation</b></p><p>Bring experimental AI models into <b>production-grade systems</b>.<br/>Validate model performance, reliability, and safety.<br/>Implement reproducibility, versioning, and automated retraining pipelines.</p><p>8. <b>Mentorship &amp; Leadership</b></p><p>Mentor junior AI engineers and researchers.<br/>Establish <b>best practices</b> for AI experimentation, deployment, and RAG/agentic architectures.<br/>Contribute to <b>AI roadmap and enterprise AI strategy</b>.</p><p><b>Required Experience</b></p><p>5+ years AI / Deep Learning / Research &amp; Production experience.<br/>Mandatory: Hands-on experience in LLM training pipelines, including cloud GPU infrastructure, data labeling, preprocessing, training, and post-processing.<br/>Deep knowledge of <b>Neural Networks, Transformers, LLMs, Vision Transformers, Diffusion Models, Agentic AI, RAG/self-RAG pipelines</b>.<br/>Hands-on experience with <b>SAM models</b>, <b>tabular data ML</b>, and preprocessing pipelines.<br/>Experience managing <b>staging, dev, and production environments</b> with <b>SSH deployment, editing, and Git/GitHub workflow</b>.<br/>Strong <b>Computer Vision and Image Processing</b> skills.<br/>Experience with <b>agent orchestration, LangChain, LangGraph, and protocol optimization</b>.<br/>Proven <b>problem-solving, critical thinking, and innovative AI solution delivery</b>.<br/>Experience integrating AI into <b>front-end and back-end systems</b>.</p><p><b>Preferred Experience</b></p><p>Experience with <b>generative AI, diffusion models, multi-modal agents</b>.<br/>Optimizing <b>LLM memory, inference, and fine-tuning at scale</b>.<br/>Experience in <b>production RAG pipelines and knowledge-augmented AI systems</b>.</p><p>Application Question(s):</p><ul><li>Are you available to work from 2:00 PM &ndash; 11:00 PM shift Monday to Friday with alternate Saturday?</li></ul><ul><li>Are you comfortable working onsite from Bahria Phase 4, Rawalpindi?</li></ul><ul><li>How many years of experience do you have as AI Engineer?</li></ul><p>Work Location: In person</p>

Lead AI Engineer

<div>Overview:
<div><b>
WHAT YOU DO AT AMD CHANGES EVERYTHING</b></div><div>
At AMD, our mission is to build great products that accelerate next-generation computing experiences&mdash;from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you&rsquo;ll discover the real differentiator is our culture. We push the limits of innovation to solve the world&rsquo;s most important challenges&mdash;striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. <b>Together, we advance your career.</b></div>
Responsibilities:
<div><b>
THE ROLE</b></div><div>
We are seeking a highly skilled systems engineer to architect and design scalable AI/HPC clusters with specific focus on rack and data center power delivery. This role involves evaluating and selecting compute, storage, networking, and power delivery components and solutions to optimize performance and reliability across global deployments. You will collaborate with cross-functional teams to deliver cutting-edge infrastructure for AI and high-performance computing workloads.</div><div></div><div><b><br>
THE PERSON</b></div><div>
An experienced systems architect with a strong background in HPC, AI infrastructure, and data center engineering. You bring deep technical knowledge of compute, power, and networking components, a strategic mindset for system-level design, and the ability to collaborate across diverse technical domains. You thrive in fast-paced environments and are passionate about building efficient, scalable, and reliable compute platforms.</div><div><b>
Key Responsibilities:</b></div><div></div><div><br>
System Architecture &amp; Design</div><ul><li>
Design scalable AI/HPC clusters including compute, storage, and networking with specific focus on , power delivery</li><li>
Evaluate and select CPUs, GPUs, accelerators, interconnects, and memory configurations for optimal cluster performance.</li></ul><div></div><div><br>
Power</div><ul><li>
Design leading-edge power delivery solutions for high-density AI/GPU deployments.</li><li>
Define power budgets, redundancy schemes, and fault tolerance mechanisms.</li></ul><div></div><div><br>
Network</div><ul><li>
Design network topologies to maximize overall cluster performance</li><li>
Understand the network performance needs of different types of workloads</li><li>
Understand advantages and performance trade-offs of network topologies for AI/HPC clusters</li></ul><div></div><div><br>
Storage</div><ul><li>
Design and optimize storage solutions to maximize AI/HPC cluster performance</li><li>
Understand advantages and performance trade-offs of cluster storage solutions, e.g. Lustre, Ceph, etc.</li></ul><div></div><div><br>
Collaboration</div><ul><li>
Work across multiple organizations with subject matter experts from hardware, software, network, data center, and operations teams to deliver scalable, efficient, and reliable compute infrastructure.</li></ul><div></div><div><b><br>
Minimum Qualifications:</b></div><br>
<div></div><ul><li>
Experience in HPC, AI infrastructure, or data center systems engineering.</li><li>
Strong understanding of rack and data center power delivery</li><li>
Knowledge of GPU/CPU architectures, PCIe, UALink, InfiniBand, and Ethernet networking.</li><li>
Familiarity with AI/ML frameworks and workload characteristics.</li><li>
Excellent problem-solving, communication, and documentation skills.</li></ul><div></div><div><b><br>
Preferred Qualifications:</b></div><br>
<div></div><ul><li>
Experience in HPC, AI infrastructure, or data center systems engineering.</li><li>
Experience designing power delivery solutions for racks and data centers</li><li>
Contributions to open-source HPC or AI infrastructure projects.</li></ul><div>
Academic Credentials</div><div>
Bachelor's or Master's degree in Electrical Engineering, COmputer Engineering, Computer Science or related field.</div><div>
 This role is not eligible for Visa Sponsorship</div><div>
#LI-KW1</div>
Qualifications:
<div><i>
Benefits offered are described: </i>AMD benefits at a glance.</div><div></div><div><i><br>
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants&rsquo; needs under the respective laws throughout all stages of the recruitment and selection process.</i></div><div></div><div><i><br>
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD&rsquo;s &ldquo;Responsible AI Policy&rdquo; is available </i><i>here.</i></div><div></div><div><i><br>
This posting is for an existing vacancy.</i></div></div>

AI Cluster & Data Center Design Engineer

Recutify Inc.

<b>Role:</b> AI Platform Engineer (Guardrails, Observability & Evaluation Infrastructure)
<br><br>
<b>Position:</b> Contract
<br><br>
<b>Location:</b> Charlotte NC (100% onsite)
<br><br>
<b>Job Description:</b><br>
AI Platform Engineer to design and build the foundational components that power enterprise-scale GenAI Applications. This includes data guardrails, model safety tooling, observability pipelines, evaluation harnesses, and standardized logging/monitoring frameworks. This role is critical for enabling safe, reliable, and compliant AI development across multiple use cases, teams, and business units. Idea is to create the common platform services that AI team will build upon.
<br><br>
Key Responsibilities
<br><br>
1. Guardrails, Safety & Governance
<br>Design and implement data guardrail frameworks (pre-processing, redaction, PII/PHI filtering, DLP integration, prompt defenses).
<br><br>
<b>Build "Model Armor" components such as:</b><br>
Input validation & sanitization
<br>Prompt-injection defenses
<br>Harmful content detection & policy enforcement
<br>Output filtering, factchecking, grounding checks
<br><br>
Integrate safety tooling (policy engines, classifiers, DLP APIs/safety models).
<br><br>
Collaborate with Security, Compliance, and Data Privacy teams to ensure frameworks meet enterprise governance requirements.
<br><br>
2. Observability Frameworks
<br><br>
Build and maintain observability pipelines using tools like Arize AI (tracing, quality metrics, dataset drift/hallucination tracking, embedding monitoring).
<br><br>
<b>Define and enforce platform-wide standards for:</b><br>
Tracing LLM calls
<br>Token usage and cost monitoring
<br>Latency and reliability metrics
<br>Prompt/model version tracking
<br><br>
Provide reusable SDKs or middleware for engineering teams to adopt observability with minimal friction.
<br><br>
3. Logging, Monitoring & Telemetry
<br><br>
<b>Design standardized LLM-specific logging schemas, including:</b><br>
Inputs/outputs
<br>Model metadata
<br>Retrieval metadata
<br>Safety flags
<br>User context and attribution
<br><br>
Build monitoring dashboards for performance, cost, anomalies, errors, and safety events.
<br><br>
Implement alerting and SLOs/SLIs for LLM inference systems.
<br><br>
4. Evaluation Infrastructure
<br><br>
Architect and maintain evaluation harnesses for GenAI systems, including:
<br>RAG evaluation (faithfulness, relevance, hallucination risk)
<br>Summarization/QA evaluation
<br>Human-in-the-loop review workflows
<br>Automated eval pipelines integrated into CI/CD
<br><br>
Support frameworks such as RAGAS, G-Eval, rubric scoring, pairwise comparisons, and test case generation.
<br><br>
Build reusable tooling for teams to write, run, and track model evaluations.
<br><br>
5. Platform Engineering & Reusable Components
<br><br>
<b>Develop shared libraries, APIs, and services for:</b><br>
Prompt management/versioning
<br>Embedding pipelines and model wrappers
<br>Retrieval adapters
<br>Common data loaders and document preprocessing
<br>Tool/function schemas
<br><br>
Drive consistency across teams with standards, reference architectures, and best practices.
<br><br>
Review system designs across use cases to ensure alignment to platform patterns.
<br><br>
6. Collaboration & Enablement
<br>Partner with AI engineers, product teams, and data scientists to understand cross-cutting needs and convert them into reusable platform features.
<br>Create documentation, onboarding guides, examples, and developer tooling.
<br>Provide internal training (brown bags, workshops) on guardrails, observability, and evaluation frameworks.
<br><br>
Required Qualifications Technical Skills
<br><br>
5-10+ years software engineering or ML infrastructure experience.
<br>Strong Python engineering fundamentals (FastAPI, async, typing/Pydantic, testing).
<br>Experience with model safety/guardrails approaches (prompt injection defense, PII redaction, toxicity filters, policy enforcement).
<br>Hands-on with Arize AI, LangSmith, or similar LLM observability platforms.
<br>Experience creating evaluation frameworks using RAGAS, G-Eval, or custom rubric systems.
<br>Strong familiarity with vector databases (Pinecone, Weaviate, Milvus), embeddings, and retrieval pipelines.
<br>Solid understanding of LLM architectures, tokenization, embeddings, context limits, and RAG patterns.
<br>Experience in cloud (GCP preferred), Kubernetes/GE, containers, and CI/CD.
<br>Strong understanding of security, governance, DLP, data privacy, RBAC, and enterprise compliance requirements.
<br><br>
Soft Skills
<br><br>
Strong documentation and communication skills.
<br>Ability to influence engineering teams and standardize best practices.
<br>Comfortable working across multiple stakeholders-platform, security, ML engineering, product.
<br><br>
Nice to Have
<br><br>
Experience with LangChain/LangGraph or Llamalndex orchestrations.
<br>Experience with Guardrails.ai, Rebuff, Protect AI, or similar LLM security tooling.
<br>Experience with GCP Vertex AI pipelines, Model Monitoring, and Vector Search.
<br>Familiarity with knowledge graphs, grounding models, fact-checking models.
<br>Building SDKs or developer frameworks adopted across multiple teams.
<br>On-prem or hybrid AI deployment experience.

AI Platform Engineer

LinkedIn

<div>
 <strong>
  Company Description
  <br/>
  <br/>
 </strong>
 LinkedIn is the worlds largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. Were also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture thats built on trust, care, inclusion, and fun where everyone can succeed.
 <br/>
 <br/>
 <strong>
  Job Description
  <br/>
  <br/>
 </strong>
 At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.
 <br/>
 <br/>
 Join us to push the boundaries of scaling large models together. The team is responsible for scaling LinkedIn's AI model training, feature engineering and serving with hundreds of billions of parameters models and large scale feature engineering infra for all AI use cases from recommendation models, large language models, to computer vision models. We optimize performance across algorithms, AI frameworks, data infra, compute software, and hardware to harness the power of our GPU fleet with thousands of latest GPU cards. The team also works closely with the open source community and has many open source committers (TensorFlow, Horovod, Ray, vLLM, Hugginface, DeepSpeed etc.) in the team. Additionally, this team focussed on technologies like LLMs, GNNs, Incremental Learning, Online Learning and Serving performance optimizations across billions of user queries.
 <br/>
 <br/>
 Model Training Infrastructure: As an engineer on the AI Training Infra team, you will play a crucial role in building the next-gen training infrastructure to power AI use cases. You will design and implement high performance data I/O, work with open source teams to identify and resolve issues in popular libraries like Huggingface, Horovod and PyTorch, enable distributed training over 100s of billions of parameter models, debug and optimize deep learning training, and provide advanced support for internal AI teams in areas like model parallelism, tensor parallelism, Zero++ etc. Finally, you will assist in and guide the development of containerized pipeline orchestration infrastructure, including developing and distributing stable base container images, providing advanced profiling and observability, and updating internally maintained versions of deep learning frameworks and their companion libraries like Tensorflow, PyTorch, DeepSpeed, GNNs, Flash Attention. PyTorch Lightning and more and more.
 <br/>
 <br/>
 Model Serving Infrastructure: this team builds low latency high performance applications serving very large &amp; complex models across LLM and Personalization models. As an engineer, you will build compute efficient infra on top of native cloud, enable GPU based inference for a large variety of use cases, cuda level optimizations for high performance, enable on-device and online training. Challenges include scale (10s of thousands of QPS, multiple terabytes of data, billions of model parameters), agility (experiment with hundreds of new ML models per quarter using thousands of features), and enabling GPU inference at scale.
 <br/>
 <br/>
 As a Sr. Staff Software Engineer, you will have first-hand opportunities to advance one of the most scalable AI platforms in the world. At the same time, you will work together with our talented teams of researchers and engineers to build your career and your personal brand in the AI industry.
 <br/>
 <br/>
 Responsibilities:
 <br/>
 <br/>
 <ul>
  <li>
   Owning the technical strategy for broad or complex requirements with insightful and forward-looking approaches that go beyond the direct team and solve large open-ended problems.
  </li>
  <li>
   Designing, implementing, and optimizing the performance of large-scale distributed serving or training for personalized recommendation as well as large language models.
  </li>
  <li>
   Improving the observability and understandability of various systems with a focus on improving developer productivity and system sustenance.
  </li>
  <li>
   Mentoring other engineers, defining our challenging technical culture, and helping to build a fast-growing team.
  </li>
  <li>
   Working closely with the open-source community to participate and influence cutting edge open-source projects (e.g., vLLMs, PyTorch, GNNs, DeepSpeed, Huggingface, etc.).
  </li>
  <li>
   Functioning as the tech-lead for several concurrent key initiatives AI Infrastructure and defining the future of AI Platforms.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Qualifications
  <br/>
  <br/>
 </strong>
 Basic Qualifications:
 <br/>
 <br/>
 <ul>
  <li>
   BS/BA in Computer Science or related technical field or equivalent technical experience
  </li>
  <li>
   5+ years of industry experience in software design, development, and algorithm related solutions
  </li>
  <li>
   5+ years of experience programming in object-oriented languages such as Python, C++, Java, Go, Rust, Scala
  </li>
  <li>
   2+ years of experience as an architect, or technical leadership position
  </li>
  <li>
   5+ years of experience in the industry with leading / building deep learning systems
  </li>
  <li>
   Hands-on experience developing distributed systems or other large-scale systems
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  </li>
 </ul>
 Preferred Qualifications:
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 <ul>
  <li>
   MS or PhD in Computer Science or related technical discipline.
  </li>
  <li>
   10+ years of experience in software design, development, and algorithm related solutions with at least 5 years of experience in a technical leadership position
  </li>
  <li>
   10+ years of experience in an object-oriented programming language such as Python, C++, Java, Go, Rust, Scala
  </li>
  <li>
   5+ years of experience with large-scale distributed systems and client-server architectures
  </li>
  <li>
   Experience building ML applications, LLM serving, GPU serving.
  </li>
  <li>
   Co-author or maintainer of any open-source projects
  </li>
  <li>
   Expertise in machine learning infrastructure, including technologies like MLFlow, Kubeflow and large scale distributed systems
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  <li>
   Expertise in deep learning frameworks and tensor libraries like PyTorch, Tensorflow, JAX/FLAX
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  </li>
 </ul>
 Suggested Skills:
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 <ul>
  <li>
   ML Algorithm Development
  </li>
  <li>
   Machine Learning and Deep Learning
  </li>
  <li>
   Information retrieval / recommendation systems
  </li>
  <li>
   Technical leadership
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  </li>
 </ul>
 LinkedIn is committed to fair and equitable compensation practices.
 <br/>
 <br/>
 The pay range for this role is $198,000 to $326,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.
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 The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits.
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  Additional Information
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 We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.
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   Having interviews in an accessible location
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   Being accompanied by a service dog
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 Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records.
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 As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.
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</div>


Sr. Staff Software Engineer, AI Infra

PARASAIL

<div><div><p>Parasail is redefining AI infrastructure by enabling seamless deployment across a distributed network of GPUs, optimizing for cost, performance, and flexibility. Our mission is to empower AI developers with a <b>fast, cost-efficient, and scalable</b> cloud experience&mdash;free from vendor lock-in and designed for the next generation of AI workloads.</p></div><div><p><b>
Job Description:</b></p>
<p>
The Senior Software Engineer, LLM Performance plays a crucial role in delivering a competitive platform by focusing on efficiently scheduling, executing, and managing AI workloads on distributed compute systems. This role is deeply technical, spanning from low-level GPU kernels to distributed AI orchestration and Kubernetes (K8s) deployments. It is about more than optimization; it&rsquo;s about pioneering efficient infrastructure that supports AI&rsquo;s transformative role in reshaping productivity, revolutionizing industries, and addressing some of the world&rsquo;s most challenging problems. You&rsquo;ll ensure that generative AI &mdash; including large language models (LLMs), multi-modal models, and diffusion models &mdash; operates efficiently at enterprise scale while driving continuous improvements in cost, performance, and sustainability.</p>
<p><b>
Responsibilities:</b></p>
<ul>
<li><p>Add support for new LLMs, working across the stack from low-level GPU kernels to Kubernetes-based deployments.</p>
</li>
<li><p>Contribute to cutting-edge open-source LLM engines such as vLLM or SGLang to extend their capabilities and performance (e.g. use Python technologies to improve API servers or request schedulers).</p>
</li>
<li><p>Operate closer to the hardware, focusing on building and integrating solutions to boost performance and hardware utilization. For example, improve attention backends like FlashAttention or FlashInfer by contributing to their development and optimization, or by integrating their solutions into vLLM.</p>
</li>
<li><p>Improve LLM performance using advanced algorithmic solutions such as speculative decoding, quantization, or other state-of-the-art techniques. Understand the impact of such techniques in model quality.</p>
</li>
</ul><p><b>Qualifications:</b></p>
<ul>
<li><p>Expertise in GPU computing, including low-level platforms such as CUDA, ROCm, XLA, PyTorch, Jax, etc.</p>
</li>
<li><p>Background in performance analysis and optimization of AI/HPC workloads (e.g. profiling or theoretical analysis of Flops and bandwidth).</p>
</li>
<li><p>Experience in writing GPU kernels using technologies like CUDA, CUTLASS, Triton.</p>
</li>
<li><p>Strength in Python and C++.</p>
</li>
<li><p>Demonstrated contributions to open-source projects. Contributions to inference engines such as vLLM is a strong plus.</p>
</li>
<li><p>A production-oriented mindset emphasizing robust, scalable code suitable for enterprise-grade applications.</p>
</li>
<li><p>A relentless curiosity about cutting-edge AI technologies combined with a passion for solving complex problems.</p>
</li>
</ul><p><b>What You Bring to the Table:</b> We are looking for people who are eager to learn and master the lower-level compute concepts that are critical for the AI revolution. With us, your skills will not only contribute to coding but will also have a significant impact on the scalability and efficiency of AI applications at large. If you're geared up for the challenge of optimizing AI performance and eager to push our technological prowess to new heights, we're excited to welcome you aboard.</p></div></div>

Senior Software Engineer, LLM Performance

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