In practice, retrieval-augmented generation (RAG) has its own complexity: document chunking, embeddings, vector databases, retrieval strategies, evaluation harnesses, guardrails, latency and cost trade-offs. Treating RAG as “just another detail” inside a generic ML role is one of the fastest ways to ship fragile systems.
That’s where the RAG Engineer comes in—a role we see emerging again and again in real LLM platforms.
RAG in one paragraph
Retrieval-augmented generation (RAG) is a pattern where an LLM is fed additional, domain-specific context at query time, typically retrieved from a vector store or search index. Rather than relying solely on its pretraining, the model grounds its answers in your data: policies, product docs, tickets, telemetry, transaction history.
Done well, RAG reduces hallucinations, improves factual accuracy, and makes LLM systems more explainable and auditable—especially important in regulated sectors such as banking, telecoms and critical infrastructure.
Done badly, RAG turns into: “We dumped PDFs into a vector DB and hope for the best.”
What a RAG Engineer actually does
A RAG Engineer is responsible for the end-to-end quality of retrieval-augmented generation in a system. In practical terms, that means:
1. Designing retrieval pipelines
Choosing chunking strategies (by section, semantic boundaries, windowing).
Selecting embedding models appropriate for the language and domain.
Managing indexing strategies, re-indexing schedules and drift.
2. Owning vector store design and operation
Evaluating and operating vector databases (e.g. managed services, open-source solutions).
Defining metadata schemas and filters for retrieval.
Handling scale, latency, cost and security constraints.
3. Building evaluation and observability for LLM answers
Creating offline evaluation sets with labelled queries and expected behaviour.
Implementing automated evaluation harnesses (relevance, groundedness, safety).
Monitoring real usage: feedback loops, escalation patterns, failure modes.
4. Working with domain experts and risk
Collaborating with business and legal teams to define acceptable responses.
Designing guardrails and escalation paths for high-risk queries.
Documenting limitations for risk, compliance and auditors.
5. Collaborating with MLOps and platform teams
Aligning RAG pipelines with CI/CD, deployment and monitoring practices.
Ensuring compatibility with existing data platforms, APIs and identity systems.
Working with infra teams on capacity planning and cost control.
This is not just “prompt engineering.” It’s a hybrid between data engineering, ML engineering, search/relevance, and product thinking.
RAG Engineer vs ML Engineer vs MLOps
Different roles, different emphasis:
Role | Primary Focus | Typical Background |
|---|---|---|
RAG Engineer | Retrieval pipelines, LLM answer quality | Data/ML + search/relevance + product |
ML Engineer | Model training & serving, feature pipelines | ML, statistics, software engineering |
MLOps | Infra, CI/CD, monitoring, deployment | DevOps / platform / ML |
In smaller teams, one person may wear two hats. In serious enterprise platforms, it’s increasingly rare to find someone who can own all three at a senior level.
How to hire your first RAG Engineer
When you look at CVs, a few filters help:
Evidence of shipped systems – not just demos or notebooks, but live services with users.
Experience with search or recommendation systems – relevance, ranking, metrics.
Comfort with vector databases and modern data stacks – e.g. working with embeddings, indexes, retrieval APIs.
Good communication – can explain trade-offs between accuracy, latency, cost and risk.
In interviews, good prompts include:
“Design a RAG system for [your domain], from ingestion to monitoring. Where do things typically break?”
“Tell us about a time when retrieval quality was bad—how did you diagnose and fix it?”
“How would you evaluate an LLM system used by relationship managers / call centre agents / engineers?”
If you’re currently trying to staff an “ML/LLM everything” unicorn, consider splitting that role into Lead MLOps and Senior RAG Engineer. One focuses on keeping the platform stable and observable; the other on making sure the LLM actually answers questions well and safely.
RAG is not a checkbox in an architecture diagram. It’s a discipline. Treating it as such, with a dedicated role, is often the difference between a neat prototype and a reliable enterprise LLM platform.
Calimala supports CDOs, CIOs and business leaders with specialised Data & AI talent, structured engagements and delivery oversight — so critical initiatives are staffed with the right people, on time.
