Eleven specialisations, one vetting standard

Cloud engineering stopped being one job somewhere around 2019. The titles below are the ones companies actually brief us on, with the distinctions that matter kept intact.

Request engineers
Cloud and infrastructure
Cloud engineers, DevOps and SRE, FinOps
AI
GenAI, agentic AI, ML, AI infrastructure
Data
Data engineers
Customer-facing
Forward deployed engineers
Vetting
Identical standard across every role

CloudOps Network matches ten engineering specialisations: cloud engineers, DevOps and SRE, GenAI engineers, agentic AI engineers, ML engineers, AI infrastructure engineers, forward deployed engineers, data engineers, FinOps engineers, and AI engineers as a combined generalist role. Every specialisation passes the same four-stage vetting standard.

Browse by role

Cloud engineers

Landing zones, networking, identity, infrastructure as code and the guardrails everything else sits on.

  • AWS
  • Terraform
  • Networking
Cloud engineers

DevOps and SRE

Pipelines, release safety, observability, incident response and on-call that people can sustain.

  • CI/CD
  • Kubernetes
  • SLOs
DevOps and SRE

AI engineers

The combined role, for teams that need one person across retrieval, orchestration and evaluation.

  • Python
  • RAG
  • Evals
AI engineers

GenAI engineers

Retrieval quality, prompt and context engineering, output evaluation and cost per request.

  • RAG
  • Embeddings
  • Guardrails
GenAI engineers

Agentic AI engineers

Multi-step systems that call tools, hold state, fail safely and can be traced when they misbehave.

  • Tool use
  • Orchestration
  • Tracing
Agentic AI engineers

ML engineers

Training, serving, feature pipelines, drift detection and the evaluation nobody built the first time.

  • Training
  • Serving
  • Drift
ML engineers

AI product engineers

The engineers who turn a working model into a product: UX for uncertainty, feedback loops, telemetry and iteration speed.

  • Product
  • Evals
  • Telemetry
AI product engineers

AI infrastructure engineers

GPU capacity, inference serving, model gateways, throughput and the cost model underneath it all.

  • GPU
  • Inference
  • Throughput
AI infrastructure engineers

Forward deployed engineers

Engineers who sit with your customer, build the integration, and turn a pilot into a renewal.

  • Onsite
  • Integrations
  • Customer-facing
Forward deployed engineers

Data engineers

Pipelines, lakehouse architecture, data contracts, quality checks and the modelling underneath analytics and AI.

  • Spark
  • Iceberg
  • dbt
Data engineers

FinOps engineers

Bill analysis, allocation, commitment strategy and the unit economics that change pricing decisions.

  • Allocation
  • Commitments
  • Unit cost
FinOps engineers

Not sure which role you need?

This is the most common thing we help with, and it is not a sign of a badly written brief. Job titles in this market drifted apart faster than anyone agreed on definitions.

Describe the outcome you want and the system you have. We will tell you which specialisation solves it, including when the answer is fewer people than you asked for.

Describe the work instead

  • “We need an AI engineer”

    Usually means a GenAI engineer, occasionally an AI infrastructure engineer, and sometimes a data engineer first.

  • “We need DevOps”

    Often a platform-shaped cloud engineer, sometimes an SRE, depending on whether the pain is delivery or reliability.

  • “We need someone senior”

    Sometimes true. Often two mid-level engineers with a clear brief beat one expensive generalist.

Tell us what you are building

Describe the work in a few lines. We come back with a shortlist of engineers who have done it before, their availability, and a rate range. No account, no platform fee, no obligation to hire.

Request engineersSee how matching works

Most briefs get a shortlist within three business days.