Zencloud TechnologiesZencloud Technologies
Building a generative AI feature is easy to start and hard to finish. We place experienced engineers who take LLM, RAG, and agent projects from prototype to a product people can rely on, on a month-to-month basis.
You interview them and direct the work. We handle contracts, equipment, and ongoing support.
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Developers in this role build products that create content, such as text, code, images, or summaries, using foundation models. The work covers connecting models to your data and systems, designing prompts and retrieval, testing output quality, adding safety controls, and running the whole thing reliably and affordably.
The role overlaps with others but isn't the same.
| Role | Main focus | Typical output |
|---|---|---|
| Generative AI developer | Building products on top of foundation models | LLM features, RAG systems, agents, evaluation pipelines |
| ML engineer | Training and deploying predictive models | Classifiers, forecasting and recommendation models |
| Data scientist | Analysis and experimentation | Insights, models, prototypes |
| Software engineer | General application development | Apps, APIs and services that call AI features |
Generative AI developer
ML engineer
Data scientist
Software engineer
A basic API call is something most software teams can add. You'll want a specialist when you see these signs.
Demos hide problems. Real users ask messy questions, and wrong or inconsistent answers erode trust quickly.
Support articles, contracts, product documentation, and internal wikis call for retrieval, permissions handling, and source citations, not just a better prompt.
Agents that call tools, update records, or run multi-step tasks need guardrails, approval steps, and careful testing.
Usage-based pricing can grow faster than revenue. Caching, model choice, and prompt design make a large difference.
Personal data, confidentiality, and audit trails need to be designed in from the start, not added at the end.
Connecting models to your product with structured outputs, retries, fallbacks, and sensible handling of timeouts and rate limits.
Chunking and indexing your documents, choosing embeddings, combining keyword and vector search, reranking results, and returning answers with sources, so responses stay grounded in your content.
Agents that use tools and APIs to complete multi-step tasks, with limited permissions, logging, and human approval for risky actions.
Adapting a model when prompting and retrieval aren't enough, for example, to match a specific style, format, or domain task, backed by good training data and a test set.
Building test sets from real examples, scoring outputs, and catching regressions every time a prompt, model, or data source changes.
Filtering sensitive content, protecting personal data, resisting prompt injection, and validating outputs before they reach users or other systems.
Caching, routing simple requests to smaller models, trimming prompts and batching work, so the product stays fast and affordable.
Working with images, audio, and documents alongside text, where the product needs it.
Prefer a team to deliver the whole product? Explore our AI product development service, or agentic AI development for agents that work across your systems.
This is the checklist we'd want any team to work through before launch.
Pick measurable success criteria before building: accuracy, tone, response time, and cost per request.
Collect real examples, including awkward ones, and agree what a correct answer looks like.
Use retrieval and sources for factual answers, rather than relying on what the model remembers.
Treat prompts like code: review them, test them, and track changes.
Decide what happens on a timeout, a refusal, an empty retrieval, or a low-confidence answer.
Handle personal data, harmful requests, and prompt injection, and limit what an agent is allowed to do.
Keep traces of inputs, retrieved sources, and outputs (with privacy in mind), and watch quality, latency, and cost.
Route uncertain or high-stakes cases to a person, and use their corrections to improve the system.
Use these questions with us or with any provider.
| Question | What a strong answer covers |
|---|---|
| How would you reduce wrong answers in a support chatbot? | Grounding answers in retrieved sources, showing citations, declining to answer when no source is found, and measuring with a test set. They shouldn't claim errors can be removed entirely. |
| RAG or fine-tuning: how do you choose? | Start with prompting and retrieval. Fine-tune when you have enough quality examples and a test showing prompting can't reach the bar. Use retrieval for facts that change. |
| How do you test a prompt change? | Run it against a saved test set, compare scores to the previous version, and review samples by hand before release. |
| How do you defend against prompt injection? | Treat retrieved and user-provided text as untrusted, give tools least privilege, validate outputs, and require approval for risky actions. |
| How do you keep costs under control? | Caching, smaller models for simple tasks, shorter prompts, usage limits, and cost tracking per feature. |
| How do you evaluate output when there's no single right answer? | Clear scoring criteria, human review of samples, and automated scoring that has been checked against human judgment. |
How would you reduce wrong answers in a support chatbot?
RAG or fine-tuning: how do you choose?
How do you test a prompt change?
How do you defend against prompt injection?
How do you keep costs under control?
How do you evaluate output when there's no single right answer?
We choose tools to fit your product, data, and budget. Depending on the project, that can include:
Five steps, from your brief to your first contribution.
Tell us what you want to build, the data you have, the models or stack you prefer, and when you need help.
A 30-minute call to understand your goals and constraints. We'll tell you honestly if a simpler approach would serve you better.
We send profiles matched to your project, usually within about a day of the call.
Use your own questions, a pairing session, or a short exercise. Nobody joins without your approval.
We handle contracts and equipment while you set up access. Most teams have the engineer working within a week or two.
All options run month to month.
A dedicated full-time engineer inside your team, for a specific capability or a stalled AI project.
Two to five engineers with a senior lead, for a new AI product area or a move from prototype to production.
A full delivery team, including QA and delivery management, for an AI product you want owned end to end.
Learn more about our staff augmentation service or our AI Audit.
Hiring Generative AI Developers: what to know
Pricing is usually a monthly rate per engineer. It depends on seniority, specialization, time-zone needs, how long you need the engineer, and the team size. Model usage costs from your AI provider are separate. We give you a clear quote after the scoping call.
They build products that generate text, code, images, or other content using foundation models. That includes connecting models to your data, designing prompts and retrieval, testing output quality, adding safety controls, and keeping the system reliable and affordable in production.
An ML engineer usually trains and deploys predictive models, such as classifiers or forecasts. A generative AI developer mostly builds on existing foundation models, focusing on retrieval, prompts, agents, evaluation, and safety.
Traditional AI typically classifies, scores, or predicts from data. Generative AI creates new content. The risks differ too: a prediction can be wrong, while a generated answer can be fluent, confident, and wrong.
Start with good prompts and retrieval. Move to fine-tuning only when you have quality examples and a test set showing that prompting can't reach your target. For facts that change often, retrieval is usually the better route.
No one can promise that. You can reduce them by grounding answers in sources, showing citations, declining to answer without evidence, testing against real examples, and keeping people involved for high-stakes cases.
We choose based on your needs, data, and budget, across commercial and open models and the main cloud platforms.
We work under an NDA, limit access to what the work needs, and follow your rules on providers and hosting regions.
Yes. A common first step is an audit of prompts, retrieval, test coverage, latency, and cost, followed by focused fixes.
We send profiles within about a day of the scoping call if we have a match, and most teams have someone working within a week or two.
It depends on seniority, scope, and engagement length. We quote after a short call.
Tell us, and we'll replace them. You don't have to manage a performance process.