ai infrastructure engineer

Google

<div><h3 class="jobSectionHeader"><b>Minimum qualifications:</b></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><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></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"><b>About the job</b></h2><p>
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.</p><br>
<p>
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.<br>
<br>
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.</p><p><br>
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.</p></div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></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>

AI Infrastructure Engineer

Accenture

<div><p><b>YOU ARE</b></p><p>
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</p><p>
ess outcomes.</p><p></p><p><b>
THE WORK</b></p><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>
<br>
<div><p><b>EDUCATION</b></p><ul><li>
Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field</li></ul><p></p><p><b>
BASIC (REQUIRED) QUALIFICATION</b></p><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><p></p>

AI Infrastructure Architecture Analyst

<div><h3 class="jobSectionHeader"><b>Minimum qualifications:</b></h3><ul>
<li>Bachelor&rsquo;s degree or equivalent practical experience.</li>
<li>2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.</li>
<li>1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).</li>
<li>Experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).</li>
<li>Ability to communicate in French and German fluently to support client relationship management in this region.</li></ul><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></h3><ul>
<li>Master&rsquo;s degree or PhD in AI, Computer Science, or a related technical field.</li>
<li>Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google&rsquo;s Agent Development Kit (ADK)) and complex patterns like ReAct, self-reflection, and hierarchical delegation.</li>
<li>Knowledge of &quot;LLM-native&quot; metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.</li>
<li>Ability to implement secure agentic workflows incorporating MCP, tool-calling, and OAuth-based authentication.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2>
Google Cloud&rsquo;s mission is to make every business successful through AI by combining cutting-edge technology, infrastructure, and talent. AI/ML software engineers in Cloud bridge the gap between pioneering models and a massive product vehicle reaching billions. Our talent density and AI-powered tools drive rapid development, rooted in a culture of empowerment and a bias to action. In this role, you aren&rsquo;t just building technology; you&rsquo;re shaping the frontier of enterprise and driving the evolution of advanced models. It's an exciting time to join Google Cloud&rsquo;s Go-To-Market team, leading the AI revolution for businesses worldwide. You&rsquo;ll excel by leveraging Google's brand credibility&mdash;a legacy built on inventing foundational technologies and proven at scale. We&rsquo;ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We&rsquo;re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era&mdash;the market is yours.</div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Serve as the lead developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, model context protocol (MCP) servers) that drive measurable return on investment.</li>
<li>Architect and code the &quot;connective tissue&quot; between Google&rsquo;s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.</li>
<li>Build high-performance evaluation (Eval) pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.</li>
<li>Identify repeatable field patterns and technical &quot;friction points&quot; in Google&rsquo;s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.</li>
<li>Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.</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>

Partner Forward Deployed Engineer III, Generative AI (French, German)

LiveKit

<div><h2 class="jobSectionHeader"><b>About LiveKit</b></h2><p>
LiveKit is building the infrastructure layer for the agentic era of computing. Our platform gives developers everything they need to build, test, deploy, scale, and observe AI agents in production. Founded in 2021, LiveKit powers voice and agentic AI applications for OpenAI, Salesforce, Spotify, Meta, and tens of thousands of other developers, collectively facilitating billions of calls each year.</p><p></p><h2 class="jobSectionHeader"><b>
About This Role</b></h2><p>
LiveKit is seeking a Forward Deployed Engineer (FDE) to work directly with customers building real-time communication and voice AI applications on the LiveKit platform.</p><p>
This role sits at the intersection of engineering, architecture, and customer implementation. Forward Deployed Engineers partner closely with customers to design systems, prototype solutions, and solve complex technical challenges as teams move from experimentation to production.</p><p>
You'll work alongside customers' engineering teams to help them successfully deploy and scale applications built on LiveKit &mdash; designing architectures, building integrations, debugging production issues, and developing reference implementations that demonstrate best practices.</p><p>
This is an ideal role for engineers who enjoy solving real-world problems in real customer environments. You'll collaborate closely with Product and Engineering to surface insights from customer implementations and help shape the future of the LiveKit platform.</p><p>
Your mission: make it seamless for developers to build and scale real-time audio, video, and AI applications on LiveKit.</p><p></p><h2 class="jobSectionHeader"><b>
You'll Thrive Here If You:</b></h2><ul><li><p>
are obsessed with helping developers succeed</p></li><li><p>
are known as the go-to person for solving tricky technical issues</p></li><li><p>
work hard and can move fast without sacrificing quality</p></li><li><p>
care deeply about polish, detail, and clear communication</p></li><li><p>
are a fast learner, frequently picking up new tools, systems, and skills</p></li></ul><h2 class="jobSectionHeader"><b>
What You'll Do</b></h2><ul><li><p>
Work directly (remotely or on-site when necessary) with customer engineering teams to design and implement real-time applications built on LiveKit</p></li><li><p>
Design scalable architectures for voice AI, real-time media, and developer platform use cases</p></li><li><p>
Build prototype integrations, reference implementations, and sample applications to accelerate customer development</p></li><li><p>
Help customers navigate SDKs, APIs, infrastructure, and deployment strategies for production systems</p></li><li><p>
Debug and troubleshoot complex technical issues in production environments</p></li><li><p>
Lead technical workshops and architecture sessions with developers and product teams</p></li><li><p>
Translate customer needs into technical insights that inform product and platform development</p></li><li><p>
Partner with Product and Engineering to improve developer experience, documentation, and tooling</p></li><li><p>
Contribute code, examples, or improvements to LiveKit's open-source ecosystem when helpful</p></li><li><p>
Act as a trusted advisor helping customers successfully launch and scale applications on LiveKit</p></li></ul><h2 class="jobSectionHeader"><b>
Who You Are</b></h2><ul><li><p>
Strong engineering background with 5+ years of experience in backend or infrastructure engineering</p></li><li><p>
Proficient in one or more programming languages such as Go, Python, Node, Rust, or C++</p></li><li><p>
Comfortable working across APIs, SDKs, distributed systems, and cloud infrastructure</p></li><li><p>
Experience building or integrating developer platforms or real-time systems</p></li><li><p>
Able to quickly understand complex systems and debug issues in real-world environments</p></li><li><p>
Strong communicator who can collaborate directly with customer engineering teams and internal product teams</p></li><li><p>
Comfortable operating in fast-paced, low-process environments where ownership and initiative matter</p></li><li><p>
Excited to work hands-on with customers solving complex technical implementation challenges</p></li></ul><h2 class="jobSectionHeader"><b>
Nice to Have</b></h2><ul><li><p>
Experience with WebRTC, real-time media systems, or telephony/SIP infrastructure</p></li><li><p>
Familiarity with voice AI, conversational agents, or AI infrastructure</p></li><li><p>
Experience working in customer-facing engineering roles such as Solutions Engineer, Field Engineer, or Forward Deployed Engineer</p></li><li><p>
Experience contributing to open-source projects or developer ecosystems</p></li><li><p>
Previous experience building with or integrating LiveKit</p></li></ul><h2 class="jobSectionHeader"><b>
Our Commitment to You</b></h2><ul><li><p>
An opportunity to build something truly impactful to the world</p></li><li><p>
Contribute to open source alongside world-class engineers</p></li><li><p>
Competitive salary and equity package</p></li><li><p>
Health, dental, and vision benefits</p></li><li><p>
Flexible vacation policy</p></li></ul><p></p><p><i>
LiveKit is an equal opportunity employer and does not discriminate on the basis of any characteristic protected by applicable law. If you require a reasonable accommodation during the application or interview process, please contact recruiting@livekit.io.</i></p><p>
Compensation Range: &pound;126K - &pound;185K</p></div>

Forward Deployed Engineer

<div><p><b>YOU ARE</b></p><p></p><p>
As a hands-on Infrastructure Architect, you are an experienced engineer with several years in infrastructure engineering who now takes on more complex, higher-impact work designing and optimizing the AI and machine learning infrastructure that powers real-world applications. Working alongside senior architects and engineers &mdash; and increasingly leading your own workstreams &mdash; you apply proven skills in coding, testing, configuring, deploying, monitoring, and troubleshooting AI systems and the infrastructure they run on. Day to day, you architect and optimize infrastructure components, write and review code and deployment scripts, design and tune cloud and on-premises compute resources such as GPU clusters and distributed training environments, deploy AI systems and models into production, and build and optimize data pipelines that feed AI and ML workflows. You optimize the computational stack for performance, cost, power, and scalability, monitor AI systems and infrastructure health across both InfraOps and MLOps disciplines, perform AI monitoring to track model and system performance, and independently troubleshoot and resolve complex issues across the stack. You also mentor junior engineers, contribute to architectural decisions, and help establish best practices. This is a hands-on, ownership-driven where you apply and deepen your 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 business outcomes.</p><p></p><p><b>
THE WORK</b></p><ul><li>
Write, review, and debug code, scripts, and infrastructure-as-code for AI infrastructure, automation, and tooling, setting standards for quality across the team.</li><li>
Architect, configure, and provision compute resources across cloud and on-premises environments, including GPU clusters and distributed training setups, optimizing for performance and utilization.</li><li>
Design and maintain deployment automation and CI/CD pipelines to support reliable, repeatable releases of AI systems, models, and applications.</li><li>
Deploy AI systems, models, and data pipelines into production, defining and improving the processes and best practices others follow.</li><li>
Lead container orchestration and model serving using tools such as Docker, Kubernetes, and model deployment frameworks.</li><li>
Architect and optimize the computational stack for performance, power, cost, and scalability, balancing trade-offs against business goals.</li><li>
Evaluate and select tools, frameworks, and platforms, making recommendations that shape the infrastructure roadmap.</li><li>
Integrate AI models and systems into existing enterprise systems, ensuring interoperability, security, and regulatory compliance.</li><li>
Own AI monitoring and infrastructure health across InfraOps and MLOps, tracking performance, reliability, and utilization, and driving remediation.</li><li>
Independently troubleshoot and resolve complex issues across the computational stack &mdash; hardware, networking, software, and models &mdash; and lead root-cause analysis.</li><li>
Mentor junior engineers and lead code reviews, providing technical direction and supporting their growth.</li><li>
Define and document architecture standards, processes, and procedures, and apply security, cost-efficiency, and scalability best practices across the infrastructure.</li></ul></div>
<br>
<div><p><b>EDUCATION</b></p><ul><li>
Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field</li></ul><p></p><p><b>
BASIC (REQUIRED) QUALIFICATION</b></p><ul><li>
Experienced with 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>
Proficient in programming languages such as Python, Java, or C++.</li><li>
Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Kubeflow).</li><li>
Strong problem-solving skills and ability to work in a fast-paced environment.</li><li>
Excellent communication and collaboration skills.</li><li>
Experience in AI/ML infrastructure engineering or related roles on a hyperscaler platform for deploying large scale solutions.</li></ul></div>

AI Infrastructure Architecture Specialist

Ciklum

<p></p><div><div><div><div><div><div><div><div><div><div><div><b>Ciklum</b> is looking for a <b>Expert Agentic AI Engineer</b> to join our team full-time in the UK .<br>
We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live.</div><p><b>
About the role:</b></p><p>
As a Expert Agentic AI Engineer, become a part of a cross-functional development team engineering experiences of tomorrow.</p><p><b>
Responsibilities:</b></p><ul><li>
Design, build, train, and deploy production-grade Machine Learning, Deep Learning, or Natural Language Processing (NLP) models</li><li>
Optimize ML models for enterprise-scale latency, throughput, and performance, ensuring flawless integration with data warehouses and automated workflow bots</li><li>
Partner with Data Engineers to engineer high-quality data pipelines, automated feature stores, and secure data preprocessing routines</li><li>
Establish and scale MLOps workflows to handle model lineage tracking, code versioning, automated validation gates, and continuous deployment pipelines</li><li>
Set the core technical direction for AI engineering across squads, evaluate emerging AI methodologies, and provide high-level mentorship to senior team members</li></ul><p><b>
Requirements:</b></p><ul><li>
5+ years (Senior) or 8+ years (Expert) of professional experience developing, shipping, and maintaining machine learning models in live production environments</li><li>
Agentic Frameworks: Hands-on experience building autonomous workflows, custom plugins, and orchestration logic using Microsoft Copilot Studio or semantic kernels</li><li>
Cloud AI Infrastructure: Proven experience deploying and fine-tuning generative AI models, prompt flow, and vector databases within Azure AI Foundry (formerly Azure AI Studio)</li><li>
Tool Integration: Strong background in connecting LLMs to external APIs, enterprise data sources, and robotic process automation (RPA) tools to enable autonomous action execution</li><li>
Elite proficiency in Python paired with extensive experience using core ML libraries and frameworks (such as PyTorch, TensorFlow, Scikit-Learn, or Hugging Face)</li><li>
Hands-on experience working with MLOps tracking tools and cloud-based AI infrastructure ecosystems (such as MLflow, Kubeflow, AWS SageMaker, or Azure ML)</li><li>
Strong computer science fundamentals, including advanced knowledge of data structures, algorithmic complexity, and clean code principles. a formal degree in Computer Science, Data Science, Mathematics, or a highly quantitative field is strongly preferred</li></ul><p><b>
What`s in it for you?</b></p><ul><li>
Strong community: Work alongside top professionals in a friendly, open-door environment</li><li>
Growth focus: Take on large-scale projects with a global impact and expand your expertise</li><li>
Tailored learning: Boost your skills with internal events (meetups, conferences, workshops), Udemy access, language courses, and company-paid certifications</li><li>
Endless opportunities: Explore diverse domains through internal mobility, finding the best fit to gain hands-on experience with cutting-edge technologies</li><li>
Care: Healthcare, Basic Life Insurance, Short and Long-term disability insurance according to the Company&rsquo;s Benefit Plans</li></ul><p><b>
About us:</b></p><p>
At Ciklum, we are always exploring innovations, empowering each other to achieve more, and engineering solutions that matter. With us, you&rsquo;ll work with cutting-edge technologies, contribute to impactful projects, and be part of a One Team culture that values collaboration and progress. In the UK, Ciklum is growing fast&mdash;inviting experienced professionals to lead digital transformation alongside Fortune 500 clients. Be part of a company where innovation and impact go hand in hand.</p><p><b>
Explore, empower, engineer with Ciklum!</b></p><p>
Interested already? We would love to get to know you! Submit your application. We can&rsquo;t wait to see you at Ciklum.</p><p>
#LI-VH1</p></div>
</div></div></div></div></div></div><div></div><div><div><div><div><div><div><p>REQUIRED SKILLS</p>
<div><ul><p>Agentic AI</p>
</ul></div></div></div></div></div></div></div><div></div><div><div><div><div><div><div><div>
</div></div></div></div></div></div></div><div><div><div><div><div><div><p>JOB INFO</p>
<div><div><div><ul><div><div>Job Identification</div>
<div>3619</div>
</div><div><div>Job Category</div>
<div>Intelligent Automation</div>
</div><div><div>Posting Date</div>
<div>07/03/2026, 02:34 AM</div>
</div><div><div>Locations</div>
<div> <div></div><div><div>London
</div></div>(Hybrid)</div></div></ul></div></div></div></div></div></div></div></div></div></div></div></div><p></p>

Expert Agentic AI Engineer

Foundation Partners Group

<div><p><b>Lead DevOps Engineer</b> Central London (Hybrid, 2&ndash;3 days/week) | Up to &pound;150,000 | Just out of seed</p><p>
Own secure, multi-tenant infrastructure end-to-end, from GitOps to compliance controls, in a hands-on, fast-paced role shaping one of the most ambitious applications of AI today.</p><p><b>
The opportunity</b></p><p>
This is a rare shot to join right at the inflection point. Our client just closed their seed round and is now scaling fast: a team of just 15 building an AI operating system for a demanding, security-conscious industry. A unified intelligence layer that connects evidence, reasoning, and action across a complex, high-stakes lifecycle where getting it right matters enormously.</p><p>
The founding team includes operators who spent years deploying production AI inside some of the world's most demanding enterprise environments. They know what it takes to make ambitious technology hold up under real pressure, and they're now applying that standard to a domain that's ripe for transformation. This is early enough that what you build becomes the foundation everything else stands on, and senior enough that you'll be working alongside people who've done this at the highest level before.</p><p>
The systems being built run inside security-conscious, compliance-bound enterprises, where the infrastructure underneath has to be as trustworthy as the intelligence layered on top of it.</p><p>
The company now needs a Lead DevOps Engineer to own that infrastructure end to end. At a team of 15, this isn't a role where you sit in a queue of tickets. You'll be the single, clear owner of how the company deploys, operates, secures, and scales, across its own platform and across isolated customer environments where the bar for trust is absolute. It's a huge amount of responsibility and autonomy, which is exactly the appeal.</p><p><b>
What you'll own</b></p><ul><li><p>
The GitOps deployment backbone on Argo CD, so a small team can ship continuously and safely across many clusters</p></li><li><p>
Kubernetes on AWS (EKS) across the fleet of production environments</p></li><li><p>
Infrastructure-as-code in Terraform/OpenTofu and Terragrunt, the source of truth for everything the company runs</p></li><li><p>
Multi-tenant, per-customer isolation: standing up a fully isolated, compliant environment for each customer, with its own cluster and data plane, and deploying to it with confidence. This is the hardest and most important part of the job.</p></li><li><p>
Security and compliance posture: secrets management, access controls, infrastructure hardening, and the audit-grade controls customers will hold you to</p></li><li><p>
Observability: telemetry, metrics, logging, tracing, dashboards, and alerting that keep a complex distributed system legible</p></li><li><p>
Reliability, incident response, and the operational runbooks that protect customer trust</p></li><li><p>
CI/CD and release automation that keep the shipping cadence high</p></li><li><p>
Developer experience: making it fast and painless for engineers to deploy and debug</p></li></ul><p><b>
Who we're looking for</b></p><p>
The ideal candidate will have come from a FAANG, Anthropic, OpenAI, Palantir type organisation, or from an exceptional AI startup, where they have personally owned and built the DevOps function while remaining hands on. This isn't someone who managed a team that did the work. It's someone who did the work themselves at a high standard, and knows what world class infrastructure looks like because they've built it before.</p><ul><li><p>
At least 4 years in DevOps, platform engineering, SRE, or infrastructure</p></li><li><p>
You've owned production systems rather than only contributing to them, including multi-tenant or customer-specific environments, and you understand the complexity that comes with them</p></li><li><p>
Deep fluency with Kubernetes, a major cloud (AWS preferred), GitOps, and infrastructure-as-code, plus the judgment to know when not to add another layer</p></li><li><p>
You come from somewhere that held a high bar, and you expect engineering excellence from yourself and the systems around you</p></li><li><p>
You're hands-on and have no interest in a purely architectural role. You explain complex tradeoffs simply and don't over-engineer.</p></li><li><p>
You treat speed, security, cost, and reliability as constraints that all apply at once, and you've struck that balance before</p></li><li><p>
Bonus: you've operated under formal security or compliance regimes (SOC2, HIPAA, or similar) and know how to make controls a strength rather than a tax</p></li></ul><p><b>
Why this role</b></p><ul><li><p><b>
Ground floor of something huge</b>: 15 people, large seed funding, and a mandate to build the infrastructure backbone from the ground up</p></li><li><p><b>
Real ownership</b>: you're not one of many DevOps hires. You're the DevOps hire</p></li><li><p><b>
Central London</b>, hybrid 2&ndash;3 days a week: flexibility without losing the energy of an in-person team</p></li><li><p><b>
Up to &pound;150,000</b>, reflecting the seniority and trust this role carries</p></li></ul><p>
This is a high-intensity, high-stakes role. The team moves quickly and gives direct feedback, the responsibility you carry will likely outrun your title, and you'll go wherever the work needs you most, which sometimes means unglamorous tasks that matter more than anything else that week. The people who thrive here find that kind of ownership and growth exciting, and they weren't looking for a nine-to-five.</p></div>

Lead DevOps Engineer (AI Early Stage Startup)

Explore 319+ Ai infrastructure engineer in London this July. Find roles across top companies and apply now to boost your career.