Generative AI engineers
The engineers who get a system from convincing to dependable. Most of that work is retrieval quality, evaluation and context discipline, and almost none of it is prompt wording.
- Typical seniority
- Mid-level through staff
- Core stack
- Python, model APIs, vector and hybrid search, rerankers
- Evaluation
- Offline suites, LLM-as-judge, human review loops
- Engagement shapes
- Fixed-scope assessment, then embedded
- Typical duration
- Six weeks to nine months
- Deliverable
- A system your team can evaluate and extend
Generative AI engineers from CloudOps Network build production systems on top of foundation models: retrieval architecture, chunking and embedding strategy, hybrid search and reranking, evaluation harnesses, output guardrails, and cost and latency engineering. They are matched on shipped production systems rather than prototypes.
What GenAI engineers do on our engagements
Retrieval architecture
Chunking strategy, embedding choice, hybrid search, reranking, and the document cleaning that sets the quality ceiling.
Context engineering
What goes into the window, in what order, at what token cost, and what gets dropped when it does not fit.
Evaluation harnesses
A regression suite for behaviour so you can tell whether this week's change helped or quietly broke last month's fix.
Guardrails
Input and output policy, injection resistance, PII handling, refusal behaviour and a documented escalation path.
Cost and latency
Model routing, caching, batching and streaming, so unit economics work at your actual volume.
Production operations
Tracing, quality dashboards, drift alerting and a runbook someone on call can follow.
Where GenAI projects actually break
In four years of this work, the failure is almost never the model. It is the material the model is being asked to read, and the absence of any way to measure whether a change helped.
A good GenAI engineer will spend the first week on your documents and your evaluation set, which can look like slow progress and is the opposite.
Document quality
Scanned PDFs, inconsistent structure and stale content set a ceiling no model clears.
No evaluation set
Without one, every release is a vibe check and regressions are invisible.
Retrieval, not generation
When answers are wrong, the right chunk usually was never retrieved.
Cost discovered late
Context bloat is the single biggest driver of unit cost, and it is easy to fix early and painful to fix late.
A sample of the bench
Availability changes weekly. This is the shape of who is in the network, not a live list.
Illustrative profiles drawn from the current roster. Names and clients are withheld until a mutual NDA is in place; the shortlist you receive names real people with real references.
Questions about hiring GenAI engineers
Is a GenAI engineer the same as a prompt engineer?
No. Prompt wording is a small part of the work. The bulk is retrieval architecture, evaluation, data preparation, guardrails and cost engineering, which is why we match on shipped systems rather than prompt portfolios.
How long until we have something in production?
A bounded assistant or retrieval system commonly reaches production in eight to sixteen weeks, including the evaluation work. Systems touching regulated data take longer because sign-off takes longer.
Do you build evaluation if we already have a system?
Yes, and it is usually the first thing we do. Changing an AI system without evaluation is guessing with extra steps.
Which model providers do your engineers work with?
The major commercial providers and open-weight models, self-hosted or through a cloud platform. Engineers are matched on architecture experience rather than loyalty to one provider.
Can you reduce our current inference costs?
Usually yes. Routing, caching and context discipline commonly take a meaningful share out of the bill without a measurable quality change, and the evaluation harness is what proves that.
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.
Most briefs get a shortlist within three business days.