GenAI Engineer: What is their role in a generative AI project?

GenAI Engineer: What is their role in a generative AI project?

Building an AI assistant seems simple. Sometimes, it’s just a matter of connecting an interface to a model like GPT, Claude, Gemini, or Mistral.

But this initial demo does not yet make a product.

A generative AI solution must address a business need. It must access the right data, respect user rights, and produce reliable responses. It must also remain fast, secure, and cost-effective.

This is where the GenAI Engineer.

This role handles the technical side of a generative AI project. They turn a use case into an application that can be tested, deployed, and monitored over time.

Their role is not limited to writing prompts. The GenAI Engineer works from defining the need to production monitoring.

What is a GenAI Engineer?

A GenAI Engineer is an engineer specializing in building applications based on generative AI models.

They don’t always develop the model itself. In most projects, they use a pre-trained model—whether from OpenAI, Anthropic, Google, Mistral, or another provider.

Their work mainly involves integrating this model into a useful product.

The GenAI Engineer builds everything around the model:

• the rules sent to the model;
• data access;
• calls to business tools;
• the application logic;
• security controls;
• quality tests;
• cost and error monitoring.

The lifecycle of a generative AI application indeed covers planning, prompt management, evaluation, deployment, monitoring, and tracing.

The GenAI profession thus sits at the intersection of software development, data, and artificial intelligence.

GenAI Engineer, AI Developer, and Machine Learning Engineer: What Are the Differences?

The boundaries vary from one company to another. In small teams, all three roles may even be combined.

The Machine Learning Engineer typically works on predictive models, datasets, and training pipelines. They may build or fine-tune a model tailored to the company’s needs.

The traditional developer builds the application, its interfaces, APIs, and business logic.

The GenAI developer bridges these two worlds. They integrate generative models into real applications, managing their limitations, costs, and behavior.

The GenAI Engineer is often a GenAI developer with a broader scope. They work on architecture, data, evaluation, and production deployment.

The GenAI Engineer starts by framing the use case

An AI project does not begin with choosing a model.

It begins with a specific problem.

The company may want to reduce time spent on customer requests. It might need to extract information from contracts. Or it may aim to help teams draft reports or process files.

The GenAI Engineer helps translate this need into a technical task.

They seek to answer several straightforward questions:

• Who will use the tool?
• On what data?
• What output should it produce?
• What should it do in case of uncertainty?
• What level of error is acceptable?
• How will the gains be measured?

This phase prevents building a generic chatbot with no real value.

It also helps determine whether AI is truly necessary. A business rule or a standard automation may suffice. Our guide on choosing between AI agent and automation helps make this distinction.

For larger projects, work on scoping and prioritising AI projects makes it possible to compare impact, feasibility, data and risks before launching a pilot.

They design the solution’s architecture

Once the need is validated, the GenAI Engineer defines the architecture.

They must decide how users access the service. The AI can be integrated into a web app, an internal tool, a CRM, Slack or Microsoft Teams.

They also choose how the application communicates with the model. This connection often goes through an API.

But the model is just one component.

A generative AI architecture can also include:

• a database;
• an authentication system;
• a search engine;
• a vector database;
• business APIs;
• a monitoring tool;
• a control layer.

The generative AI engineer’s role is to make this whole system work without creating an unnecessarily heavy architecture.

They select the model suited to the use case

There is no single best model for all projects.

One model may excel at writing. Another may be better at extracting data. A smaller model may also be cheaper and faster.

The GenAI Engineer therefore compares several options.

They assess response quality, speed, cost, context limits and data-related constraints. They also check whether the model can be hosted on a private infrastructure.

This choice must be based on real-world tests.

A good benchmark uses requests similar to those of future users. It is not limited to a few pre-prepared questions for a demo.

He builds the prompts and the orchestration logic

The prompt defines the instructions given to the model.

It can specify the AI’s role, the expected format, and the rules to follow. It can also instruct the model to refuse a response when data is missing.

But the prompt alone is not enough.

The GenAI Engineer often builds an entire chain. The application receives a request, retrieves information, adds context, calls the model, and then verifies the result.

This logic is known as orchestration.

For simple workflows, a few API calls may suffice. For richer projects, the generative AI engineer may use LangChain, LangGraph, LlamaIndex, or a custom-built solution.

He connects the AI to the company’s data

A general-purpose model does not know a company’s internal documents.

It is unaware of its contracts, procedures, or customer tickets. The right information must therefore be provided.

A common method is RAG, or retrieval-augmented generation.

The system first searches for relevant passages in the documents. It then passes these passages to the model, which can produce a response based on the retrieved sources.

The GenAI Engineer sets up this chain.

He manages document collection, chunking, indexing, and search. He must also maintain sources and access rights.

Our article on LangChain RAG architecture details the key components of such a system. Production RAG architectures indeed separate data processing, search, generation, and access controls.

He enables the AI to use tools

Some applications need to go beyond a simple response.

An assistant may need to check inventory, create a task, or update a CRM. It may also trigger an automation.

The GenAI Engineer then defines the permitted actions.

Each tool must have a clear role. Parameters must be controlled. Sensitive actions may sometimes require human validation.

Standards like the Model Context Protocol can help connect an assistant to data and APIs more cleanly.

The goal is not to grant full access to the model. Only the necessary functions should be exposed.

They assess the quality of the responses

A generative AI application should not be validated based on a first impression.

A smooth response can be wrong. An accurate response can be too long. An assistant may work for ten questions and fail on the next ones.

The GenAI Engineer therefore creates a test suite.

This suite includes real requests, expected answers, and edge cases. It may contain incomplete requests, missing documents, or malicious instructions.

The engineer then measures several elements:

• accuracy;
• relevance;
• adherence to sources;
• output format;
• refusal rate;
• speed;
• cost per request.

Evaluations must occur before deployment. They must also continue after launch. Current tools allow tracking quality, relevance, security, and grounding of responses.

They secure the application

Risks do not come solely from the model.

They can stem from data, access rights, or connected tools. A poorly defined rule may expose a document to the wrong person.

The GenAI Engineer thus collaborates with security and legal teams.

They limit the data sent to the model. They protect API keys. They filter inputs and outputs. They log useful actions.

They also anticipate LLM-specific attacks. A user may attempt to alter system prompts or force access to information.

Security must be considered from the start. The NIST framework recommends integrating governance, measurement, and risk management throughout the lifecycle of an AI system.

They deploy and monitor the product

When tests are successful, the GenAI Engineer prepares production release.

They configure environments, deployment, and access. They also plan a fallback solution in case of model failure.

After launch, their work continues.

They monitor errors, latency, token volume, and costs. They analyze queries that produce poor responses. They refine prompts, data sources, and models.

This phase is often called GenAIOps. It adapts DevOps and MLOps methods to generative AI applications.

Without this monitoring, a successful demo can quickly turn into an unstable product.

What are the skills of a GenAI Engineer?

The GenAI Engineer skills remain primarily development skills.

This profile typically masters Python or TypeScript. They know how to build APIs, use Git, and deploy applications to the cloud.

They also understand how LLMs work. They are familiar with tokens, context windows, embeddings, RAG, and tool calls.

GenAI Engineer skills also cover databases, security, and testing.

Finally, this profile must understand the business.

A good GenAI Engineer can communicate with a product manager, a domain expert, and a technical team. They must translate constraints into simple choices.

Who does the GenAI Engineer work with?

The GenAI Engineer does not work alone.

They collaborate with business teams to define use cases. They work with the Product Manager on user journeys and success criteria.

They also partner with developers, Data Engineers, security experts, and infrastructure teams.

In an SME, the same person may cover several of these roles.

In a large company, GenAI roles are often split among specialists. The GenAI Engineer retains a central role, connecting the model, the application, and business needs.

When does a company need this profile?

A GenAI Engineer becomes essential once a project goes beyond the occasional use of a public tool.

This applies when a company wants to connect AI to its data, integrate an assistant into its tools, or automate decisions.

This profile is also needed when a prototype must become a real product.

An application built with Lovable, Bolt, Cursor, or Replit can help validate an idea. But its architecture, security, and tests must be reviewed before production. Our analysis vibe coding: AI prototype or real product? explains this passage.

From use case to reliable AI product

A GenAI Engineer does more than just plug a model into an interface.

They frame the need, select the architecture, and connect the data. They then build the tests, security rules, and monitoring system.

This approach is what turns an idea into a product.

At Scroll, we support companies from scoping to deployment. We design, in particular, AI assistants connected to internal data, business tools, and RAG architectures.

The goal isn’t to add AI everywhere. It’s about creating a useful, measurable, and easy-to-maintain solution.