AI Software & Network Jobs Recruitment Specialists
Hamilton Barnes specialises in AI software and network recruitment, helping organisations hire the engineers building the next generation of AI infrastructure. From AI Platform Engineers and Software Engineers to Network Architects, HPC Engineers and MLOps specialists, we connect employers with exceptional permanent and contract talent.
The AI Software and Networking Market
The rapid adoption of production AI has created an entirely new market for specialist software and networking professionals. As organisations move AI models from research into live production, demand has surged for MLOps Engineers, AI Platform Engineers, AI Network Engineers and HPC specialists capable of designing, deploying and scaling AI infrastructure.
The rapid adoption of production AI has created an entirely new market for specialist software and networking professionals. As organisations move AI models from research into live production, demand has surged for MLOps Engineers, AI Platform Engineers, AI Network Engineers and HPC specialists capable of designing, deploying and scaling AI infrastructure.
The result is one of the most competitive recruitment markets in technology. Employers are investing heavily to secure experienced AI software and networking talent, while skilled professionals remain in exceptionally short supply.
Technical Skills Employers Are Looking For
AI software and networking roles require a unique blend of software engineering, platform infrastructure and high-performance networking expertise. Some of the most in-demand technical skills include:
- AI software and MLOps: PyTorch, TensorFlow, Kubeflow, MLflow, Weights & Biases, Airflow, feature store tooling (Feast, Tecton), model serving frameworks (Triton, TorchServe, vLLM), LLMOps tooling
- AI networking: InfiniBand, RoCE, RDMA, NCCL, high-bandwidth fabric design, BGP at scale, SR-IOV, network telemetry and observability, Arista and Mellanox environments
- Platform and infrastructure: Kubernetes, Slurm, Ray, CUDA, containerised ML workloads, infrastructure as code (Terraform, Pulumi), cloud AI platforms (AWS SageMaker, GCP Vertex, Azure ML)
- Data engineering: Apache Spark, Kafka, dbt, Flink, data lakehouse architectures, real-time feature pipelines, vector databases for AI applications
Professionals who combine AI software engineering, platform infrastructure and AI networking experience remain among the most sought-after technology specialists. Organisations are increasingly competing for engineers who have successfully deployed and operated AI systems at production scale.
AI Software and Network Roles We Recruit
We recruit specialist professionals across the AI software and networking ecosystem, supporting organisations from fast-growing AI startups to hyperscale infrastructure providers with permanent and contract talent.
AI Software Engineer Jobs
Builds and maintains the software systems supporting AI workloads in production: model serving infrastructure, feature pipelines, and the API layers that bridge research and deployment. One of the most competed-for profiles in the current technology market.
MLOps Engineer Jobs
Owns the operational infrastructure for machine learning - CI/CD for models, training pipeline automation, experiment tracking, and the monitoring systems that keep production ML healthy.
AI Platform Engineer Jobs
Designs and operates the internal platforms that enable ML and data science teams to work at scale - compute orchestration, toolchain standardisation, and resource management across GPU and CPU environments.
AI Network Architect Jobs
Designs network topology for AI cluster environments - InfiniBand and RoCE fabric design, RDMA configuration, and high-bandwidth east-west traffic management. One of the most technically specialised roles in the market.
AI Developer / AI Engineer Jobs
Builds AI-powered applications and systems - integrating foundation models into products, building inference APIs, and owning the software layer between models and end users. Among the fastest-growing role categories in the technology sector.
Our Data Centers and AI Sub-Specialisms
AI Data Center
The physical and operational infrastructure layer for AI workloads - GPU clusters, high-density power, liquid cooling, and the deployment and operations engineering that keeps AI facilities running. The highest-growth segment of the data center market.
Data Center IT
IT operations inside data center environments - servers, storage, hardware deployment, infrastructure support, and the technical disciplines managing compute and network environments within the white space.
Critical Facilities
The engineering and operations disciplines that maintain uptime across live data center facilities - power, cooling, compliance, BMS, and the 24/7 operational roles that underpin facility performance and resilience.
Construction
The build phase of the data center lifecycle - project management, M&E engineering, commissioning, and the construction disciplines delivering new facilities and expansion programmes across the UK and globally.
Our Featured AI Jobs
Explore the AI Software & Network Timeline
Gain a deeper understanding of how the AI Software & Network market has evolved between 2016 and 2026. Use our Interactive Timeline to explore major technology trends, industry growth, and the key developments that have shaped the sector over the past decade.
Meet The Team
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Latest Insights
The Right Role Is Out There. Let's Find It.
Whether you're an engineer looking for your next move in AI software or networking, or an organisation building out a production AI platform team, we work across the full spectrum of this market.
Frequently Asked Questions
What types of AI software and network roles do you cover?
We cover the full range of disciplines in this specialism: ai engineer jobs, MLOps engineers, ai platform engineer roles, AI network architects, data pipeline engineers, distributed systems architects, SDN specialists, and ai developer positions. Both permanent and contract engagements are placed.
How is an AI software engineer role different from a standard software engineering position?
An ai software engineer working in production AI environments is expected to understand model serving infrastructure, training pipeline architecture, and the operational complexity of running ML systems at scale - not just general application development. These roles require familiarity with ML frameworks, orchestration tooling, and often GPU compute environments, which places them in a distinct skills market from traditional software engineering.
What does an AI platform engineer actually own?
The ai platform engineer function typically owns the internal developer platform that enables data science and ML teams to work efficiently - compute orchestration (Kubernetes, Slurm, Ray), toolchain standardisation, environment management, and resource allocation across GPU and CPU infrastructure. At larger organisations, this function has direct influence over infrastructure cost and model deployment velocity.
What networking skills are specific to AI environments?
AI network architecture requires familiarity with InfiniBand and RoCE fabric design, RDMA configuration, high-bandwidth east-west traffic management, and NCCL optimisation - disciplines that do not feature meaningfully in traditional enterprise or data center networking roles. Engineers with this background are among the most competed-for technical profiles in the current market.
Do you place AI developers and AI engineers as well as more specialised roles?
Yes. AI developer and engineer jobs span a wide range of seniority and scope - from engineers building inference APIs and integrating foundation models into products, through to architects designing the software systems that underpin large-scale AI deployments. We work across that full range.