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 may seem straightforward. 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 deliver 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 specialising 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 tracking.

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 by company. In smaller 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, retrieve information from contracts, or help teams draft reports or process files.

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

They seek to answer several simple 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 benefit be measured?

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

It also helps determine whether AI is truly necessary. A business rule or traditional automation may suffice. Our guide on choosing between AI agents and automation helps clarify this distinction.

For larger projects, a scoping and prioritization of AI projects helps compare impact, feasibility, data requirements, 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. 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 system work without creating an unnecessarily heavy architecture.

They select the model suited to the use case

No single model is best for all projects.

One model may excel at writing. Another may be better at data extraction. A smaller model may also cost less and respond faster.

The GenAI Engineer therefore compares several options.

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

This choice must be based on real-world tests.

A good benchmark uses requests close to those of future users. It doesn’t rely on a few pre-prepared questions for a demo.

They build the prompts and orchestration logic

The prompt defines the instructions given to the model.

He can define the AI’s role, the expected output format, and the rules to follow. He can also instruct it to refuse a response when data is missing.

But the prompt alone is not enough.

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

This logic is known as orchestration.

For simple workflows, a few API calls may suffice. For more complex projects, the GenAI Engineer can 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 does not know 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 to the model, which can produce an answer grounded in the retrieved sources.

The GenAI Engineer sets up this pipeline.

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

Our article on RAG architecture with LangChain 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 purpose. Parameters must be controlled. Sensitive actions may sometimes require human approval.

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

MCP is an open specification, versioned by date: it describes a client-server protocol where a server exposes tools, resources and prompts, which any compatible client can discover without specific integration. The value for a team isn’t the technical novelty, it’s reversibility: a connector written once remains usable if the model or assistant changes. The specification is public: the Model Context Protocol specification.

The goal isn’t to grant full model access. Only the necessary functions should be exposed.

They assess response quality

A generative AI application shouldn’t 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.

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 factors:

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

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

They secure the application

Risks don’t come only from the model.

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

The GenAI Engineer therefore works 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 attacks specific to LLMs. A user may attempt to alter system instructions or force access to information.

This risk has a name and a rank: prompt injection tops the OWASP Top 10 for LLM Applications. The key point, often misunderstood, is that it is not always user-driven: so-called indirect injection occurs via content the model reads, a indexed document, a web page, an email, that contains instructions for the model rather than the reader. A RAG system thus mechanically expands the attack surface to everything it indexes. Reference: OWASP LLM01, prompt injection.

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

This framework, published on January 26, 2023, is voluntary and is structured around four functions: govern, map, measure, manage. It was supplemented on July 26, 2024, by a generative AI profile, NIST AI 600-1, which catalogs risks specific to these systems and corresponding actions. For a product team, this profile is more directly actionable than the general framework: it addresses hallucination, training data leakage, and misuse, not AI in the abstract. Documents: the NIST AI Risk Management Framework and its generative AI profile (PDF).

They deploy and monitor the product.

Once tests are successful, the GenAI Engineer prepares for 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 track errors, latency, token volume, and cost. They analyze queries that yield poor responses. They refine prompts, 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 skills does a GenAI Engineer need?

The GenAI Engineer skills are first and foremost development skills.

This role typically involves proficiency in Python or TypeScript, the ability to create APIs, use Git, and deploy applications to the cloud.

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

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

Finally, this role must understand the business.

A strong GenAI Engineer can communicate with product managers, domain experts, and technical teams. They must translate constraints into simple decisions.

Who does the GenAI Engineer work with?

The GenAI Engineer does not work alone.

They collaborate with business teams to define use cases, and 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, one person may cover several of these roles.

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

When does a company need this role?

A GenAI Engineer becomes essential once a project goes beyond 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 role 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 transition.

From use case to reliable AI product

The GenAI Engineer does more than connect a model to an interface.

They define the requirements, choose the architecture, and connect the data. They then build the tests, security rules, and monitoring system.

This is the approach that turns an idea into a product.

At Scroll, we support companies from scoping to deployment. We design, among other things,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.