Platform Engineer

Stealth iT Consulting; Greater London, Manchester, Glasgow; Hybrid; GBP 42,000 - 70,000; Full time

Benefits offered by this job

Bonus & Benefits

Job summary

Stealth iT Consulting is seeking an AI Platform Engineer (Senior Consultant) to design, build, and run the infrastructure underpinning enterprise AI workloads across UK locations. You will work on GPU-accelerated compute, Kubernetes/OpenShift, and cloud-native services in hybrid environments, focusing on scalable, secure AI platforms.

The role requires active SC or SC Eligibility and offers a salary up to £70,000 plus bonus and benefits, with opportunities to mentor teams and shape AI platform

Qualifications

• Hands-on with AI/GenAI platform engineering.
• Experience across multi-cloud environments.
• Strong knowledge of Kubernetes and container platforms.
• Familiarity with MLOps/LLMOps tooling and CI/CD.
• Experience with observability, security, and governance for AI systems.

Responsibilities

• Design, build, and operate AI platform infrastructure (GPU compute, container platforms).
• Architect and deploy AI-ready infrastructure across cloud, on-prem, and hybrid.
• Develop model serving, gateway, and orchestration components.
• Implement MLOps/LLMOps pipelines using IaC and GitOps.
• Lead workshops, reviews, and provide technical guidance to teams.

Skills

AI platform engineering

Cloud platforms

Kubernetes

GitOps

MLOps

Tools

OpenShift

AKS/EKS/GKE

Terraform

Ansible

Job description

AI Platform Engineer - Senior Consultant - SC Eligibility required - Permanent

Locations: London, Manchester, Glasgow + other UK locations

Salary: Up to £70,000 + Bonus & Benefits

Active SC or SC Eligibility essential

As an AI Platform Engineer, you’ll design, build, and operate the infrastructure that enterprise AI and Generative AI workloads run on: the platform layer beneath LLMs, agents, and MLOps pipelines. This spans GPU-accelerated compute and container platforms, model serving and gateway infrastructure, evaluation and guardrail systems, and the MLOps/LLMOps tooling that takes a model from experiment to production. You’ll work across hybrid and multi-cloud environments, helping clients modernize their AI infrastructure and adopt AI safely and at scale.

As part of your role, you will:

• Be a senior or lead engineer on client AI platform engagements
• Architect and deploy AI-ready infrastructure (GPU-accelerated compute, Kubernetes/OpenShift, and cloud-native services) across cloud, on-premises, and hybrid environments
• Build and operate core AI platform components: model serving and gateway infrastructure, agent orchestration and tool-calling frameworks, evaluation harnesses, and guardrail/governance layers
• Implement MLOps and LLMOps pipelines (model deployment, monitoring, retraining, and fine-tuning where relevant) using Infrastructure-as-Code, GitOps, and CI/CD
• Establish observability, security, and governance frameworks specific to AI systems, including cost attribution and lifecycle management
• Work with clients and internal teams to develop new opportunities and shape a strong AI platform engineering culture
• Lead client workshops, architecture reviews, and technical briefings; provide operational support including monitoring and troubleshooting
• Share your knowledge and experience with colleagues as you coach and mentor them, while developing your own skills by experimenting with and learning new technologies

You’ll bring deep, hands-on experience in most of the areas below, with strong depth in AI/GenAI platform engineering specifically. You don’t need to tick every box.

• Model serving and gateway infrastructure (e.g. vLLM, LiteLLM, managed endpoints), with routing, failover, and per-workload cost attribution
• Agent orchestration and tool-calling frameworks (e.g. LangGraph or equivalent), including familiarity with the Model Context Protocol (MCP)
• Guardrail and AI-observability tooling (e.g. NeMo Guardrails, OpenTelemetry GenAI conventions, LangSmith, Braintrust)

MLOps & LLMOps

• Hands-on with MLOps platforms (Azure ML, Databricks, SageMaker) and vector/retrieval databases (Pinecone, Milvus, pgvector)
• Experience with GPU-accelerated infrastructure and NVIDIA AI Enterprise or equivalent stacks
• Exposure to fine-tuning, RLHF, or SLM distillation is a strong plus

Cloud-Native & Infrastructure

• Deep expertise in Kubernetes and container platforms (OpenShift, AKS, EKS, GKE, or VMware Tanzu)
• Infrastructure as Code and DevOps practices (Terraform, Bicep, Ansible, GitOps and CI/CD pipelines)
• 5+ years’ experience across Azure, AWS, or GCP; strong DevOps fundamentals