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Rivac AI Labs
Engineering & Software Development

RAG Pipeline Engineer

Most RAG systems that underperform aren't failing because of the model, they're failing because of chunking that splits context badly, retrieval that surfaces the wrong documents, or no re-ranking step to fix it before generation. We build production retrieval-augmented-generation pipelines end to end: chunking strategy, hybrid search, re-ranking, and eval-driven tuning against your actual document set and query patterns. This is for teams whose RAG feature gives plausible-sounding but wrong answers, or who are building one from scratch and want retrieval quality treated as the hard problem it is, not an afterthought. You get a pipeline measured against a real eval set, with retrieval quality you can quantify rather than guess at.

This is a capability we deliver for you as a service, not a role we’re hiring for.

How We’d Approach This

A clear, staged plan, not a black box

These are the service-specific steps inside our six-step process.

  1. 1

    Analyze your document corpus and real query patterns to diagnose where retrieval is likely to fail.

  2. 2

    Build a pilot pipeline (chunking, embedding, retrieval, re-ranking) against a representative query set.

  3. 3

    Review retrieval quality metrics with your team and tune chunking and ranking based on measured gaps.

  4. 4

    Deploy the tuned pipeline with the eval set in place so future document or query changes stay measured.

What You Get

Deliverables from this engagement

  • Production RAG pipeline (chunking, retrieval, re-ranking)
  • Evaluation set and retrieval-quality metrics report
  • Hybrid search configuration tuned to your document set
  • Documentation for maintaining and extending the pipeline

Six Ways We Could Architect This

Different engagement, different build, pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Read all 6 architectures as text

Lean Fast-Start Architecture

Document Ingestion

  • Source Documents
  • Simple Fixed Size Chunking
  • Embedding Model Call

Vector Store

  • Single Vector Index

Query Time

  • User Query
  • Embed Query
  • Top K Similarity Search
  • Stuff Context Into Prompt
  • LLM Generates Answer

Basic Eval

  • Small Eval Set of Q and A Pairs
  • Manual Answer Review
  • Log Retrieval Misses for Review

Cloud-Native / Managed Services Architecture

Managed Ingestion Pipeline

  • Document Loader Service
  • Semantic Chunking Service
  • Managed Embedding API

Managed Vector Infrastructure

  • Managed Vector Database
  • Metadata Store

Hybrid Retrieval

  • Keyword Search Index
  • Vector Similarity Search
  • Hybrid Score Merger
  • Managed Re Ranking Model

Generation and Eval

  • LLM Generation Endpoint
  • Automated Eval Pipeline
  • Retrieval Quality Dashboard

Event-Driven & Automation-First Architecture

Ingestion Triggers

  • New Document Upload Event
  • Content Change Webhook
  • Scheduled Recrawl Job

Automated Ingestion Pipeline

  • Ingestion Queue
  • Chunking Worker
  • Embedding Worker
  • Vector Index Upsert

Query Time Automation

  • Query Event
  • Hybrid Retrieval Function
  • Re Ranking Function
  • Generation Function

Automated Feedback Loop

  • Log Query and Answer
  • Nightly Eval Job Against Golden Set
  • Auto Alert on Regression

Enterprise-Grade & Compliance-Ready Architecture

Governed Ingestion

  • Document Source With Access Labels
  • PII and Sensitivity Scanner
  • Chunking With Access Metadata Preserved
  • Embedding Service

Secure Storage

  • Encrypted Vector Store
  • Per Document Access Control Lists

Permission Aware Retrieval

  • Query With User Identity
  • Access Filtered Retrieval
  • Re Ranking Model
  • LLM Generation With Citation

Audit and Governance

  • Full Query and Retrieval Audit Log
  • Immutable Log Store
  • Compliance Review Dashboard

AI-Augmented, Human-in-the-Loop Architecture

AI Retrieval and Drafting

  • User Question
  • Hybrid Retrieval Over Corpus
  • Re Ranking Model Selects Top Passages
  • LLM Drafts Answer With Citations

Confidence Check

  • Answer Confidence and Citation Coverage Score
  • Confidence Above Threshold

Human Review Gate

  • Subject Matter Expert Reviews Draft
  • Expert Approves Answer
  • Expert Edits Answer or Flags Bad Retrieval
  • Escalation Queue for Ambiguous Cases

Approved Delivery

  • Approved Answer Sent to User
  • Rejected Cases Logged for Retrieval Tuning

Integration-Heavy / Ecosystem-Connected Architecture

Connected Knowledge Sources

  • Confluence and Wiki
  • Google Drive and SharePoint
  • Support Ticket History
  • Product Documentation Site

Unified RAG Pipeline

  • Connector Sync Layer
  • Chunking and Embedding Service
  • Vector Store
  • Hybrid Retrieval and Re Ranking

Ecosystem Consumers

  • Support Chatbot
  • Internal Search Portal
  • Slack Bot Integration
  • CRM Embedded Assistant

More in Engineering & Software Development

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  • AI Data Remediation Engineer

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  • Data Engineer

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Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.

Talk to us about RAG Pipeline Engineer

Most engagements like this start as a $500–$2,500 pilot, see full pricing.

Questions? Book a free call