AI & Network Infrastructure Engineer
| Dyddiad hysbysebu: | 12 Awst 2025 |
|---|---|
| Cyflog: | £45,000 i £48,000 bob blwyddyn |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 11 Medi 2025 |
| Lleoliad: | Milton Keynes, Buckinghamshire |
| Gweithio o bell: | Ar y safle yn unig |
| Cwmni: | GAK ENTERPRISES LTD |
| Math o swydd: | Parhaol |
| Cyfeirnod swydd: | AIANIE_GAK_08_2025_02 |
Crynodeb
Key Responsibilities
Design, implement, and optimise network infrastructures that support AI workloads, high-performance computing, and cloud-native applications.
Develop and deploy AI/ML-based monitoring and automation tools to improve network resilience, predictive maintenance, and fault detection.
Integrate AI algorithms with network management systems for intelligent routing, load balancing, and anomaly detection.
Manage data pipelines for AI models, ensuring secure, reliable, and efficient network transport.
Collaborate with data scientists, software engineers, and cloud architects to deploy AI workloads in edge, on-prem, and hybrid cloud environments.
Implement software-defined networking (SDN) and network function virtualisation (NFV) in telco and enterprise environments.
Optimise network designs for GPU clusters, InfiniBand/RoCE, and high-bandwidth AI training environments.
Drive automation using Python, Ansible, Terraform, or similar tools to streamline provisioning and reduce manual intervention.
Monitor and analyse network and AI system performance, providing recommendations for continuous improvement.
Stay up to date with AI networking trends, emerging technologies, and industry standards.
Required Skills & Experience
Strong foundation in network engineering: IP/MPLS, OSPF/BGP, VLANs, QoS, VPNs, Layer 2/3 protocols.
Experience with cloud networking (AWS, Azure, GCP) and hybrid cloud architectures.
Hands-on experience in network automation (Python, Ansible, Terraform).
Working knowledge of AI/ML concepts and deployment processes.
Familiarity with SDN/NFV principles and telco cloud environments.
Understanding of GPU-based AI infrastructure, high-performance networking (InfiniBand, RoCE), and data centre interconnects.
Experience with Linux administration, containerisation (Docker, Kubernetes), and orchestration.
Proven ability to work in multi-vendor environments (Cisco, Juniper, Huawei, Nokia, Arista).
Strong problem-solving skills and the ability to work independently.
Desirable Skills
Exposure to AI model optimisation for real-time inference at the network edge.
Experience with AI monitoring tools (e.g., Prometheus + AI anomaly detection).
Understanding of data centre design fundamentals.
Experience in AI-driven network security (threat detection, intrusion prevention).
Familiarity with telecom standards and 5G network architectures.
Benefits
Competitive salary + performance bonus
20 days annual leave + bank holidays
Company pension scheme
Private health and wellness benefits
Training and development opportunities in AI and networking
Hybrid and flexible working arrangements
Design, implement, and optimise network infrastructures that support AI workloads, high-performance computing, and cloud-native applications.
Develop and deploy AI/ML-based monitoring and automation tools to improve network resilience, predictive maintenance, and fault detection.
Integrate AI algorithms with network management systems for intelligent routing, load balancing, and anomaly detection.
Manage data pipelines for AI models, ensuring secure, reliable, and efficient network transport.
Collaborate with data scientists, software engineers, and cloud architects to deploy AI workloads in edge, on-prem, and hybrid cloud environments.
Implement software-defined networking (SDN) and network function virtualisation (NFV) in telco and enterprise environments.
Optimise network designs for GPU clusters, InfiniBand/RoCE, and high-bandwidth AI training environments.
Drive automation using Python, Ansible, Terraform, or similar tools to streamline provisioning and reduce manual intervention.
Monitor and analyse network and AI system performance, providing recommendations for continuous improvement.
Stay up to date with AI networking trends, emerging technologies, and industry standards.
Required Skills & Experience
Strong foundation in network engineering: IP/MPLS, OSPF/BGP, VLANs, QoS, VPNs, Layer 2/3 protocols.
Experience with cloud networking (AWS, Azure, GCP) and hybrid cloud architectures.
Hands-on experience in network automation (Python, Ansible, Terraform).
Working knowledge of AI/ML concepts and deployment processes.
Familiarity with SDN/NFV principles and telco cloud environments.
Understanding of GPU-based AI infrastructure, high-performance networking (InfiniBand, RoCE), and data centre interconnects.
Experience with Linux administration, containerisation (Docker, Kubernetes), and orchestration.
Proven ability to work in multi-vendor environments (Cisco, Juniper, Huawei, Nokia, Arista).
Strong problem-solving skills and the ability to work independently.
Desirable Skills
Exposure to AI model optimisation for real-time inference at the network edge.
Experience with AI monitoring tools (e.g., Prometheus + AI anomaly detection).
Understanding of data centre design fundamentals.
Experience in AI-driven network security (threat detection, intrusion prevention).
Familiarity with telecom standards and 5G network architectures.
Benefits
Competitive salary + performance bonus
20 days annual leave + bank holidays
Company pension scheme
Private health and wellness benefits
Training and development opportunities in AI and networking
Hybrid and flexible working arrangements