ai infrastructure engineer

Openkyber

<div>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.
For applications and inquiries, contact: hirings@openkyber.com</div>

OCI AI Infrastructure Engineer

NiCE

<div><div>At NiCE, we don&rsquo;t limit our challenges. We challenge our limits. Always. We&rsquo;re ambitious. We&rsquo;re game changers. And we play to win. We set the highest standards and execute beyond them. And if you&rsquo;re like us, we can offer you the ultimate career opportunity that will light a fire within you.</div><div><h2 class="jobSectionHeader">
Overview</h2>

We&rsquo;re looking for an AI Engineer with strong expertise in Voice, Network, and Compute infrastructure automation. You will design and deploy AI‑driven automation that improves reliability, performance, and operational efficiency across large‑scale systems. This role emphasizes agent‑based automation, MCP‑style service frameworks, and reusable Skills that enable intelligent workflows across infrastructure platforms.
<h2 class="jobSectionHeader">
What You&rsquo;ll Do</h2>
<ul><li>Build AI/ML‑powered automation for Voice, Network, and Compute platforms.</li>
<li>Design and deploy Model Context Protocol (MCP)&ndash;style servers or similar agent service layers that expose infrastructure capabilities as secure, reusable AI‑callable actions.</li>
<li>Develop Skills and tools for workflow automation, enabling LLM‑driven agents to execute operational tasks, diagnostics, and remediation safely and deterministically.</li>
<li>Create intelligent systems for anomaly detection, forecasting, root cause analysis, and self‑healing.</li>
<li>Integrate automation with SBCs, SIP systems, routers/switches, load balancers (F5, Palo Alto, Arista), VMware, Kubernetes, and cloud compute platforms.</li>
<li>Build APIs, Skills, and agent workflows that connect with observability, CMDB, and ticketing platforms (Grafana, Prometheus, ServiceNow, etc.).</li>
<li>Implement Infrastructure‑as‑Code using Terraform, Ansible, or similar tools.</li>
<li>Partner with operations teams to eliminate manual work, improve uptime, and accelerate incident response.</li>
<li>Define guardrails, permissions, and validation layers to ensure AI‑driven automation operates safely in production environments.</li>
</ul><h2 class="jobSectionHeader">What You Bring</h2>
<ul><li>5+ years of experience in AI/ML engineering, automation engineering, or infrastructure engineering.</li>
<li>Strong Python development skills, including API and service development.</li>
<li>Hands‑on experience with:
<ul><li>Voice: SIP, SBCs, call flows</li>
<li>Network: routing/switching, F5, Palo Alto, Arista</li>
<li>Compute: VMware, Kubernetes, and cloud platforms</li>
</ul></li><li>Experience building or operating MCP‑like services, agent backends, or tool‑calling frameworks that allow LLMs to interact with real systems.</li>
<li>Proven ability to create modular Skills or tools for workflow automation and agent execution.</li>
<li>Experience with ML frameworks and LLM orchestration tools.</li>
<li>Solid understanding of CI/CD, GitOps, containers, and orchestration.</li>
</ul><h2 class="jobSectionHeader">Bonus Skills</h2>
<ul><li>Cloud‑native automation experience (AWS Lambda, Azure Functions, GCP Cloud Functions).</li>
<li>Knowledge of Kafka or other event/streaming frameworks.</li>
<li>Experience with policy engines, RBAC, and safety controls for AI automation.</li>
<li>Networking, cloud, or automation certifications.</li>
</ul><h2 class="jobSectionHeader">Why This Role Matters</h2>

You will help build the next generation of intelligent, self‑driving infrastructure, where AI agents, MCP‑style services, and reusable Skills replace fragile scripts and manual runbooks. Your work will reduce toil, improve system reliability, and enable teams to operate infrastructure at scale with speed, safety, and resilience.</div><div>
About NiCE
NICE Ltd. (NASDAQ: NICE) software products are used by 25,000+ global businesses, including 85 of the Fortune 100 corporations, to deliver extraordinary customer experiences, fight financial crime and ensure public safety. Every day, NiCE software manages more than 120 million customer interactions and monitors 3+ billion financial transactions.
Known as an innovation powerhouse that excels in AI, cloud and digital, NiCE is consistently recognized as the market leader in its domains, with over 8,500 employees across 30+ countries.
NiCE is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, age, sex, marital status, ancestry, neurotype, physical or mental disability, veteran status, gender identity, sexual orientation or any other category protected by law.</div></div>

AI Automation Infrastructure Engineer

Valeo

<div>
 Valeo est une entreprise mondiale de haute technologie qui con&ccedil;oit des solutions r&eacute;volutionnaires pour r&eacute;inventer la mobilit&eacute;. Nous sommes un &eacute;quipementier partenaire des constructeurs automobiles et des acteurs de la nouvelle mobilit&eacute; dans le monde entier. Notre vision ? Inventer une mobilit&eacute; plus verte et plus s&ucirc;re, gr&acirc;ce &agrave; des solutions ax&eacute;es sur la conduite intuitive et la r&eacute;duction des &eacute;missions de CO2. Nous sommes leaders dans nos domaines d'activit&eacute; et reconnus comme l'une des plus grandes entreprises innovantes au monde.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Deploy and maintain secure cloud infrastructure primarily on GCP (Google Cloud Platform) and AWS, ensuring seamless integration between services.
 </li>
 <li>
 Manage and optimize GKE (Google Kubernetes Engine) clusters for high-availability AI applications and microservices.
 </li>
 <li>
 Infrastructure as Code~ Build and enforce Terraform strategies to provision and manage infrastructure, ensuring environments are reproducible and version-controlled.
 </li>
 <li>
 CI/CD &amp; Software Verification~ Design and implement advanced CI/CD workflows using GitHub Actions, moving beyond simple deployments to create intelligent automation.
 </li>
 <li>
 Build robust verification pipelines that include automated testing, linting, security scanning, and quality gates before production release.
 </li>
 <li>
 Streamline the release process for backend and frontend applications, ensuring "one-click" reliability.
 </li>
 <li>
 Oversee the deployment, maintenance, and backup strategies for databases.
 </li>
 <li>
 Implement comprehensive monitoring and logging solutions (Prometheus, Grafana, Cloud Ops, Cloud Monitoring) to ensure system health, performance, and rapid incident response.
 </li>
 <li>
 Implement security best practices (IAM, VPC configuration, encryption) to protect sensitive AI data and intellectual property.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 
 
 Education &amp; Experience
 
 
 
 
 <ul>
 <li>
 B.Sc. or M.Sc. in Computer Science, Computer Engineering, or a related technical field.
 </li>
 <li>
 5+ years of relevant experience in DevOps, Cloud Engineering, or Site Reliability Engineering (SRE).
 </li>
 <li>
 Proven experience acting as a Senior or Lead engineer, guiding architectural decisions.
 
 
 </li>
 </ul>
 
 
 Technical Requirements
 
 
 
 
 <ul>
 <li>
 Advanced hands-on experience with GCP (specifically GKE, VPC, IAM) and a working knowledge of AWS.
 </li>
 <li>
 Proven ability to design and implement robust, scalable, and secure cloud architectures.
 </li>
 <li>
 Mastery of Docker and Kubernetes administration.
 </li>
 <li>
 Strong proficiency in Terraform.
 </li>
 <li>
 Expert knowledge of GitHub Actions for building, test, build, and deploy pipelines.
 </li>
 <li>
 Experience managing PostgreSQL and ClickHouse databases.
 </li>
 <li>
 Deep understanding of Linux System Administration.
 </li>
 <li>
 Experience with AI/ML lifecycle tools (e.g., Kubeflow, MLflow, Vertex AI).
 </li>
 <li>
 Strong proficiency in Python and Bash scripting.
 </li>
 <li>
 Familiarity with DevSecOps tools and practices.
 </li>
 <li>
 Bonus~ Official GCP Certifications (e.g., Professional Cloud Architect, Professional Cloud DevOps Engineer) and Kubernetes Certifications (e.g., CKA, CKAD, CKS) are highly preferred.
 </li>
 <li>
 Bonus~ Knowledge of Javascript/Node.js is a strong plus.
 
 
 </li>
 </ul>
 
 
 Soft Skills &amp; Mindset
 
 
 
 
 <ul>
 <li>
 Ability to design long-term solutions rather than quick fixes.
 </li>
 <li>
 Excellent ability to explain complex cloud concepts to Data Scientists and business stakeholders.
 </li>
 <li>
 Comfortable working in a fast-paced environment with evolving requirements.
 </li>
 <li>
 Fluent English is a must.
 
 
 </li>
 </ul>
 
 Position~
 
 
 
 AI4all
 
 
 
 D&eacute;partement~
 
 
 
 Product technical
 
 
 
 Schedule~
 
 
 
 Temps plein
 
 
 
 Employee Status~
 
 
 
 CDI
 
 
 
 Type de contrat~
 
 
 
 
 Job Posting Date~
 
 
 
 2026-02-03
 
 
 
 Join Us !
 
 
 Being part of our team, you will join~
 
 
 <ul>
 <li>
 one of the largest global innovative companies, with more than 20,000 engineers working in Research &amp; Development
 </li>
 <li>
 a multi-cultural environment that values diversity and international collaboration
 </li>
 <li>
 more than 100,000 colleagues in 31 countries... which make a lot of opportunity for career growth
 </li>
 <li>
 a business highly committed to limiting the environmental impact if its activities and ranked by Corporate Knights as the number one company in the automotive sector in terms of sustainable development
 
 
 </li>
 </ul>
 More information on Valeo~ https~//www.valeo.com
 
</div>

Senior DevOps Engineer, AI4ALL

C2R Ventures

<div>
 
 Elite multi-strategy hedge fund manager that invests globally in a wide array of assets and strategies, is seeking an elite Agentic AI Research Engineer to play a key role in the ongoing development and enhancement of their investment process.
 
 
 
 
 
 Do not need financial experience to apply, but of course it is welcome. Candidates from AI research arms/divisions of notable bigtech companies and/or AI start-ups with elite VC backing are very welcome to apply.
 
 
 
 
 
 This Engineer will be embedded in investment teams to augment their investment process through designing and building multi-agent frameworks by developing deep understanding of each business and leveraging cutting-edge methods.
 
 
 
 
 
 This is a Full Stack role that will encompass the building of foundational infrastructure and developing robust data pipelines through agentic design.
 
 
 
 
 
 
 Responsibilities:
 
 
 <ul>
 <li>
 Work with investment teams to find solutions to their most vexing challenges, applying agents to assist in solving problems
 </li>
 <li>
 Design, build, and integrate a multi-agent AI framework that can orchestrate deep research in and across vastly different subject matter and domain expertise
 </li>
 <li>
 Implement rigorous quantitative benchmarks for large scale agentic tasks
 </li>
 <li>
 Assist with automated evaluation of AI models and prompts across a wide variety of project and investment teams
 </li>
 <li>
 Help create and optimize data mixes for model training that maximize agent performance or ease of use on agentic tasks
 </li>
 </ul>
 
 
 
 
 
 Requirements:
 
 
 <ul>
 <li>
 0&gt;1 builder who is comfortable with uncertainty, operates with high agency, and excels at rapid iteration and shipping
 </li>
 <li>
 Experience developing complex agentic systems using LLMs with an emphasis on agentic harness design (e.g., communication architectures for agents)
 </li>
 <li>
 Successfully demonstrated the ability of taking a product from conception to completion
 </li>
 <li>
 Understanding of LLM-as-judge, reward hacking, specification gaming robustness problems
 </li>
 <li>
 Have strong Python skills and can build robust infrastructure across the stack
 </li>
 </ul>
</div>

Artificial Intelligence Engineer

Emerald Advantage Concepts LLC

<div>
 
 About The Team
 
 
 
 We build the data, evaluation, and experimentation infrastructure powering next‑generation
 
 agentic AI systems
 
 . Our work directly supports
 
 all five leading AI labs
 
 and focuses on the hardest problems in LLM reasoning, RL environments, and human‑in‑the‑loop workflows.
 
 
 We're a fast‑moving, talent‑dense team with backgrounds in
 
 quant finance, top‑tier startups, and elite engineering orgs
 
 . Revenue is already in the
 
 8‑figure range
 
 with a steep growth curve and a major Series A on the way.
 
 
 
 The Role
 
 
 
 This is a
 
 broad, high‑ownership engineering role
 
 &mdash; not a narrow feature lane.
 
 
 You'll work across research, infra, product, and data, owning systems end‑to‑end. Expect to touch everything from RL environments to distributed infra to full‑stack dashboards.
 
 
 
 A typical month might include:
 
 
 
 <ul>
 <li>
 Prototyping a new RL environment from a research paper
 </li>
 <li>
 Deploying distributed experiments on Kubernetes
 </li>
 <li>
 Improving reliability of Next.js dashboards
 </li>
 <li>
 Building a Kafka pipeline for annotator analytics
 
 
 
 </li>
 </ul>
 You'll shape core systems used by frontier AI labs from day one.
 
 
 
 What You'll Do
 
 
 
 <ul>
 <li>
 Build scalable systems: RL environments, APIs, human‑in‑the‑loop platforms
 </li>
 <li>
 Collaborate with research, product, and design to ship quickly
 </li>
 <li>
 Write clean, maintainable code with strong documentation
 </li>
 <li>
 Participate in architecture discussions and code reviews
 </li>
 <li>
 Solve real‑world scalability and reliability challenges
 </li>
 <li>
 Contribute to the infrastructure powering frontier AI evaluation
 
 
 
 </li>
 </ul>
 
 Who We're Looking For
 
 
 
 We're looking for early‑career engineers who have already shown they can thrive in fast‑moving, high‑ownership environments and want to work on some of the most challenging problems in AI.
 
 
 
 Experience
 
 
 
 <ul>
 <li>
 1&ndash;3 years as a full‑stack software engineer
 </li>
 <li>
 Background at a high‑growth startup, top quantitative trading firm, or experience as a founding engineer at a company with meaningful early traction
 </li>
 <li>
 If your experience is primarily big tech, we look for a strong CS foundation (e.g., top‑tier CS programs such as Berkeley, CMU, MIT, Stanford)
 
 
 
 </li>
 </ul>
 
 Bonus Experience
 
 
 
 <ul>
 <li>
 Time spent at companies focused on human‑in‑the‑loop AI, data labeling, or AI evaluation (e.g., Surge AI, Snorkel, Scale, Labelbox, Micro1, Mercor)
 </li>
 <li>
 Exposure to fast‑paced environments where you shipped features end‑to‑end and owned outcomes
 
 
 
 </li>
 </ul>
 
 What Matters Most
 
 
 
 <ul>
 <li>
 You've built real systems &mdash; not just maintained them
 </li>
 <li>
 You take ownership, move quickly, and enjoy solving hard technical problems
 </li>
 <li>
 You're comfortable working directly with researchers, product teams, and customers
 </li>
 <li>
 You thrive in environments where the roadmap changes based on what you learn
 
 
 
 </li>
 </ul>
 
 Technical Skills
 
 
 
 <ul>
 <li>
 Full‑stack: Next.js / React, Node.js / Python
 </li>
 <li>
 Infra: Kubernetes, Kafka, Redis, Elasticsearch
 </li>
 <li>
 Ability to build end‑to‑end systems with high ownership
 
 
 
 </li>
 </ul>
 
 Soft Skills
 
 
 
 <ul>
 <li>
 Strong ownership and bias toward shipping
 </li>
 <li>
 Comfortable being client‑facing with AI lab researchers
 </li>
 <li>
 Thrives in fast‑paced, high‑iteration environments
 
 
 
 </li>
 </ul>
 
 Work Environment
 
 
 
 
 5 days/week onsite
 
 in Financial District
 
 
 Flexible hours
 
 
 Optional half‑day or remote on Sundays
 
 
 Tight‑knit, high‑trust, high‑velocity team
 
 
 
 Why Join
 
 
 
 <ul>
 <li>
 Work directly with frontier AI labs
 </li>
 <li>
 Solve the hardest problems in AI evaluation
 </li>
 <li>
 Massive ownership and impact from day one
 </li>
 <li>
 Build at a scale most AI startups never reach
 </li>
 <li>
 Join a team of elite engineers and operators
 
 </li>
 </ul>
</div>

Software Engineer (Full‑Stack / Infrastructure) — Frontier AI Evaluation

Biohub

<div>
 Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere.
 
 
 Biohub operates one of the largest AI compute clusters dedicated to biology, spanning three frontier research institutes with some of the world's leading biologists. We're not a startup trying to find product-market fit, and we're not a pharma company optimizing a pipeline. We're building frontier AI for fundamental science, as open science, at a scale no one else is doing. This is a unique moment for scientific acceleration. The problems are among the hardest and most impactful problems you can choose to work on, and we move at a pace that meets this moment.
 
 
 
 Our Research Spans
 
 
 
 <ul>
 <li>
 Frontier molecular modeling, from protein language models (e.g., ESM) to structure prediction (e.g., ESMFold) and beyond.
 </li>
 <li>
 Scaled biological foundation models trained on some of the largest GPU clusters dedicated to science
 </li>
 <li>
 Imaging foundation models trained across the world's largest microscopy datasets
 </li>
 <li>
 Reasoning and agentic systems that connect frontier LLMs with biological foundation models
 </li>
 <li>
 Mechanistic interpretability of biological foundation models: extracting new biological knowledge directly from model weights
 </li>
 <li>
 Scientific data at unprecedented scale: AI systems to collect, curate, and learn from some of the richest biological datasets ever assembled
 
 
 </li>
 </ul>
 Join our Team!
 
 
 As an ML Engineer, you'll join some of the strongest infrastructure engineers in AI, building the systems that connect everything together. The infrastructure problems you solve directly determine what science becomes possible.
 
 
 What You'll Do
 
 
 <ul>
 <li>
 Work with high-dimensional scientific data formats and contribute to backend compatibility, format evaluation, and I/O performance benchmarking at petabyte scale.
 </li>
 <li>
 Define and shape the engineering patterns your team and collaborating researchers will build on for years; the abstractions you write today become the foundation others depend on at scale.
 </li>
 <li>
 Work at the intersection of AI systems and biological discovery, where the infrastructure problems you solve directly determine what science becomes possible.
 </li>
 <li>
 Deploy models to production and manage artifact tracking across models and datasets.
 </li>
 <li>
 Design and optimize GPU-native data loading pipelines for large-scale multi-dimensional tensor workloads, including profiling and resolving hardware utilization bottlenecks across multi-backend systems.
 </li>
 <li>
 Work on simplification and improvement of codebase abstractions to accelerate research momentum.
 </li>
 <li>
 Build and maintain primitives for pre-training infrastructure that ensure the reliability and continuity of large-scale training runs.
 </li>
 <li>
 Help cultivate best practices in MLOps, and think about the full ML lifecycle, including data, fine-tuning, deployment, reliability and monitoring.
 </li>
 <li>
 Possesses the ability to execute complex modifications to the research pipeline, such as fast data loading and distributed training.
 </li>
 <li>
 Handle DevOps responsibilities, focused on making all engineers and researchers more productive. This includes tasks like cluster monitoring, unit testing and integration testing of research codebase, and continuous integration.
 </li>
 <li>
 Collaborate with partner researchers and engineers to deploy our technology within external infrastructure.
 
 
 </li>
 </ul>
 What You'll Bring
 
 
 <ul>
 <li>
 Hands-on experience with PyTorch, including custom training loops, distributed training, or low-level performance work.
 </li>
 <li>
 Familiarity with GPU-native data I/O tools and large-scale tensor formats (e.g. Zarr, HDF5, TensorStore, or similar).
 </li>
 <li>
 Experience with distributed computing frameworks such as Apache Spark, Dask, or Ray.
 </li>
 <li>
 Familiarity with containerization and orchestration tools such as Docker and Kubernetes.
 </li>
 <li>
 Experience building or working with AI agent frameworks is a plus.
 </li>
 <li>
 A track record of building systems that other engineers and researchers depend on. Not just running experiments, but shipping infrastructure that scales.
 
 
 </li>
 </ul>
 
 Compensation
 
 
 
 The future anticipated Redwood City, CA, and New York City, NY base pay range for a role in this field is $150,000 to $350,000+ annually. Compensation ranges will vary based on job-related skills, level of experience, and knowledge. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process.
 
 
 
 Benefits For The Whole You
 
 
 
 We&rsquo;re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible.
 
 
 <ul>
 <li>
 Provides a generous employer match on employee 401(k) contributions to support planning for the future.
 </li>
 <li>
 Paid time off to volunteer at an organization of your choice.
 </li>
 <li>
 Funding for select family-forming benefits.
 </li>
 <li>
 Relocation support for employees who need assistance moving
 
 
 </li>
 </ul>
 
 Please note that applying to this opportunity does not guarantee that we will be in touch with you regarding our opportunities. Our recruiting team will contact you if your experience aligns with the skills we seek for future open positions. We will keep your interest on file, contact you as opportunities arise, and send you information about the exciting work we are doing at Biohub. You can opt out at any time!
 
</div>

Machine Learning Engineer, AI

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