AI Infrastructure

Expert Infrastructure Recruitment for Teams Building and Operating AI at Scale

DeepRec.ai supports organisations designing, building, and scaling AI infrastructure that underpins production machine learning and inference platforms in use today. Our AI infrastructure practice focuses on supporting companies hiring specialist engineers across compute, platforms, and systems, where architecture, performance, efficiency, and reliability determine whether AI systems succeed outside the lab.

As AI models move into real-world use, AI infrastructure has become the defining challenge of production AI. Organisations are under increasing pressure to provision, orchestrate, and operate compute and data platforms at scale, meeting strict requirements around latency, throughput, cost, and availability. This has driven unprecedented demand for AI infrastructure capability, and for engineers who can build and operate the systems that inference, training, and experimentation depend on.

DeepRec.ai’s recruitment consultants work closely with teams operating at this level of complexity, giving us a clear view of the skills, experience, and systems required to build production-grade AI. Whether that’s AI platform engineering, GPU and accelerator infrastructure, distributed systems, or inference at scale, we connect organisations with AI engineers who can operate effectively in real-world environments.

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Why Leading AI Teams Choose DeepRec.ai for AI Infrastructure Hiring?

DeepRec.ai's specialist Infra consultants are trusted by tech pioneers across the UK, Ireland, Germany, Switzerland, and the United States.

Our consultants work directly with teams building and operating production AI systems, giving us first-hand exposure to the architectures, constraints, and trade-offs involved.

Our consultants work directly with teams building and operating AI platforms and infrastructure in production, giving us first-hand exposure to the architectures, trade-offs, and operational realities involved.

This includes teams working on distributed training and inference, high-performance computing, GPU and accelerator clusters, and AI platform reliability, where system-level performance and infrastructure design are critical to deploying AI systems at scale.

Dedicated AI Infrastructure Delivery Teams

DeepRec.ai operates through dedicated divisions and delivery teams, each focused on a specific area of deep tech. This structure allows our AI infrastructure practice to work with depth and continuity, rather than spreading expertise across unrelated markets.

We speak Deep Tech

AI infrastructure is not a generic hiring problem. When you need to hire niche AI talent, you need a specialist who speaks deep tech. We know our serving systems from our pipelines, and we know how to talk about them with top-tier candidates. 

Cross-border hiring expertise - SECO & AUG Licensed

As part of Trinnovo Group, DeepRec.ai maintains both SECO and AUG licenses, enabling us to provide compliant cross-border recruitment and employment services across Switzerland and Germany. In addition to permanent hiring, we can payroll talent in-house and manage the full administrative and compliance burden on behalf of our clients. This is supported by an internal compliance team, ensuring hiring processes remain robust, transparent, and aligned with local regulatory requirements.

A Deep Tech Community

Much of the most in-demand AI infrastructure talent does not engage with traditional hiring channels. Through sustained involvement in the deep tech ecosystem, including events, collaboration, and research, DeepRec.ai maintains close ties to the AI infrastructure community, enabling trusted access to engineers and technical leaders who are typically difficult to reach through conventional recruitment. Find out more about DeepRec.ai's social hub here: https://www.deeprec.ai/community

A Perfect Client Net Promoter Score (+100)

DeepRec.ai maintains a client Net Promoter Score of +100 based on client feedback, a reflection of consistent delivery, clear communication, and long-term partnerships built on trust. For our clients, this typically reflects a recruitment experience that is focused, technically credible, and aligned with the realities of hiring in complex, talent-constrained deep tech markets.

AI Inference and Serving Model Efficiency

Alongside our broader AI Infrastructure division, DeepRec.ai has a dedicated team focused purely on AI inference and serving efficiency.

As AI systems move from research environments into production, inference becomes the moment of truth. Latency, throughput, cost per request, hardware utilisation, and system reliability all come under pressure at scale. The engineering challenges shift from experimentation to optimisation, from building models to operating them in live, user-facing environments.

Our inference-focused consultants work with teams building high-performance serving systems, real-time and batch inference pipelines, model optimisation frameworks, and accelerator-aware deployment environments. We support organisations hiring engineers who understand quantisation, model compression, distributed inference, GPU scheduling, and system-level efficiency.

If your priority is deploying models reliably and efficiently in production, explore our AI Inference recruitment expertise to see how we support teams operating at this level.

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Who We Partner With 

We work with organisations building, scaling, and operating AI infrastructure in production, ranging from early-stage teams establishing core platforms to scale-ups expanding distributed systems, and enterprises investing in large-scale AI compute and platform capability.

We also work closely with engineers, researchers, and technical leaders who build and operate AI infrastructure. Many of the people we support are not actively looking for new roles, but are open to conversations about work that is technically meaningful, well-resourced, and aligned with how they want to operate.

Our role is to bring these two sides together thoughtfully, matching organisations with engineers where technical context, expectations, and long-term goals are aligned.

If you're interested in exploring a fulfilling new role in AI infrastructure, learning more about current market trends, or you'd like to hire exceptional talent, our consultants are always available to support you. Please get in touch with us directly, and we'll get back to you as soon as possible: 

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AI INFRASTRUCTURE CONSULTANTS

Anthony Kelly

Co-Founder & MD EU/UK

Sam Warwick

Senior Consultant - ML Systems + AI Infra

Jacob Graham

Senior Consultant

LATEST JOBS

San Mateo, California, United States
Senior MLOps Engineer
Senior MLOps / ML Infrastructure Engineer About the Company Our client is a Series B, venture-backed deep-tech company building a Physics AI platform that helps engineering teams bring products to market faster, reduce development risk, and explore better designs with greater confidence. The platform combines large-scale simulation data with modern machine learning to generate high-fidelity predictions of physical behavior in near real time. Customers include leading organizations across aerospace, automotive, and advanced manufacturing, working on some of the most demanding real-world engineering problems. The Role This role focuses on building and operating the infrastructure that powers physics-based AI systems at scale. The position enables ML engineers and scientists to train, track, deploy, and monitor models reliably without managing low-level infrastructure. The work sits at the intersection of ML systems, cloud infrastructure, and large-scale simulation data, with a strong emphasis on performance, reliability, and developer productivity. It is a hands-on engineering role in a fast-moving, in-office environment, working closely with ML researchers, platform engineers, and product teams. What You’ll DoDesign, build, and maintain robust MLOps infrastructure supporting the full ML lifecycle, from experimentation and training through to production deployment and monitoringImplement automated training pipelines, experiment tracking, and model lifecycle management using tools such as Kubeflow, MLflow, and Argo WorkflowsDevelop scalable data pipelines capable of handling large volumes of unstructured data, particularly 3D geometric data and physics simulation outputsDeploy machine learning models into production inference systems with strong standards for performance, reliability, and observabilityManage model registries and integrate them with CI/CD workflows to support consistent and reliable model releasesImplement monitoring systems that continuously track model health and performance in productionCollaborate closely with ML researchers, platform engineers, and product teams to evolve the infrastructure platform for physics-based AI applicationsWrite production-grade code and optimize cloud infrastructure, primarily on Google Cloud Platform, while making thoughtful trade-offs around scalability, cost, and operational simplicity using Docker and KubernetesWhat We’re Looking ForBachelor’s degree or higher in Computer Science, Data Science, Applied Mathematics, or a closely related field5 years of industry experience building MLOps platforms or ML systems in production environmentsStrong proficiency in Python, with working knowledge of BASH and SQLHands-on experience with cloud infrastructure such as GCP, AWS, or AzureExperience with containerization and orchestration tools including Docker and KubernetesFamiliarity with modern MLOps frameworks such as Kubeflow, MLflow, and Argo WorkflowsExperience building and maintaining scalable data pipelines, ideally working with unstructured or high-dimensional dataAbility to independently deploy models and implement monitored inference systems in productionComfortable troubleshooting complex distributed systems and building reliable infrastructure that other teams depend onNice to HaveInterest in physics simulation, scientific computing, or HPC environmentsExperience building production MLOps platforms in deep-tech or simulation-heavy environmentsFamiliarity with additional programming languages such as Go or C Working Style and Culture This role suits someone who enjoys startup environments, learns quickly, and communicates clearly across disciplines. The team works on-site five days a week and values close collaboration, fast feedback loops, and hands-on problem solving. There is a strong belief that great infrastructure should be largely invisible, enabling engineers and scientists to move faster without friction.
Sam WarwickSam Warwick