What Skills Does a RAG Engineer Need?

Start with the information problem

Retrieval-augmented generation (RAG) uses retrieved information to give a generative model relevant context. That does not make every answer correct. Source selection, document freshness, permission boundaries and the ability to cite evidence all affect whether a system is useful.

Be specific about the material you handled. Was it structured data, a document collection or a changing knowledge base? Explain how content was prepared, chunked and indexed, how relevant passages were selected and what the application should do when the answer is not supported.

• Describe the user question and what a satisfactory answer required.
• Compare retrieval approaches against real information needs, not only a demonstration query.
• Explain how the system deals with missing, stale or restricted information.

Evaluate behavior, not only a model response

Evaluation can separate retrieval relevance from answer quality. A wrong answer may begin with irrelevant source material, a poor prompt, a model limitation or an integration fault. An LLM engineer should be able to explain which layer they measured and how a failure was investigated.

A small, reviewed set of representative questions is more useful than a single impressive example. Include edge cases, ambiguous questions, refusal conditions and a way to inspect cited sources. Production work may also require monitoring latency, cost, access and behavior after the underlying information changes.

• State what was checked manually and what was tested repeatedly.
• Document human review and escalation where a response carries material risk.
• Avoid publishing confidential prompts, documents or evaluation datasets.

Place agentic AI and MLOps in context

An agentic workflow may select tools or sequence actions, so permissions, observable steps and stopping conditions become important. Do not treat tool use as proof that a system is safe or independent. Show what the system can do, who approves consequential actions and how you tested failures.

MLOps experience can include reproducible pipelines, versioning, deployment and monitoring. When describing it alongside RAG, distinguish model operations from document ingestion and search-index maintenance. They are related concerns with different failure modes.

• Name the decisions you owned, the checks you used and the people who reviewed the outcome.
• State whether the work was a prototype, a limited pilot or an operating service.

Help employers understand your AI projects

Open with the problem, then describe the architecture in plain language: information source, retrieval, model interaction, application interface and evaluation. Follow with your contribution and one difficult trade-off, such as recall versus precision or freshness versus processing cost.

If you can share an example, sanitize the inputs and identify the boundaries of your work. Listing frameworks without describing decisions makes it hard to tell whether you designed a system, integrated an API or only explored a tutorial. A candid description is more helpful than a broad claim of AI expertise.