RAG Implementation Guide

Overview

RAG projects require a diverse skill set spanning ML engineering, backend development, and data engineering. This guide helps you build the right team.

Core Roles

1. ML Engineer / AI Engineer

Responsibilities:

Design retrieval pipeline architecture
Optimize embedding models and vector search
Implement evaluation frameworks
Fine-tune models when needed

Required Skills:

Python, PyTorch/TensorFlow
Vector databases (Pinecone, Weaviate, Qdrant)
Embedding models (sentence-transformers, OpenAI)
Evaluation metrics (Recall@k, MRR, NDCG)

Experience Level: Mid to Senior (3-5+ years ML)

Hiring Difficulty: High (competitive market)

2. Backend Engineer

Responsibilities:

Build API layer for RAG system
Implement caching and optimization
Handle production deployment
Monitor system performance

Required Skills:

Python/Node.js/Go
API design (REST, GraphQL)
Database management (Postgres, Redis)
Cloud platforms (AWS, GCP, Azure)

Experience Level: Mid-level (2-4 years)

Hiring Difficulty: Medium

3. Data Engineer

Responsibilities:

Build data ingestion pipelines
Process and chunk documents
Maintain vector database
Handle data quality and updates

Required Skills:

ETL pipelines (Airflow, Dagster)
Data processing (Pandas, Spark)
Document parsing (PDF, HTML, OCR)
SQL and NoSQL databases

Experience Level: Mid-level (2-4 years)

Hiring Difficulty: Medium

4. Product Manager (Part-time)

Responsibilities:

Define success metrics
Prioritize features
Gather user feedback
Coordinate with stakeholders

Required Skills:

Understanding of AI/ML capabilities
Data-driven decision making
User research
Roadmap planning

Experience Level: Mid to Senior

Hiring Difficulty: Medium (AI PM experience rare)

Team Sizing by Project Phase

Phase 1: MVP (3-6 months)

Team Size: 2-3 people

1 ML Engineer (full-time)
1 Backend Engineer (full-time)
1 PM (20% time)

Budget: $300k-500k (salaries + infrastructure)

Phase 2: Production (6-12 months)

Team Size: 4-6 people

2 ML Engineers
2 Backend Engineers
1 Data Engineer
1 PM (50% time)

Budget: $800k-1.2M/year

Phase 3: Scale (12+ months)

Team Size: 6-10 people

3 ML Engineers
3 Backend Engineers
2 Data Engineers
1 DevOps Engineer
1 PM (full-time)

Budget: $1.5M-2.5M/year

Skills Matrix

Skill	ML Engineer	Backend Eng	Data Engineer
Python	Expert	Proficient	Expert
Vector DBs	Expert	Basic	Proficient
LLM APIs	Expert	Proficient	Basic
System Design	Proficient	Expert	Proficient
Data Pipelines	Basic	Basic	Expert
DevOps	Basic	Proficient	Basic
Evaluation	Expert	Basic	Basic

Hiring Strategy

Where to Find Talent

ML Engineers:

AI research labs (OpenAI, Anthropic alumni)
ML bootcamps (fast.ai, deeplearning.ai)
Academic conferences (NeurIPS, ICML)
GitHub (contributors to HuggingFace, LangChain)

Backend Engineers:

Traditional tech companies
Startups with API-heavy products
Open source communities

Data Engineers:

Data platform companies
Analytics teams
ETL tool companies

Interview Process

ML Engineer:

Take-home: Build a simple RAG system (4 hours)
Technical: Optimize retrieval quality (1 hour)
System design: Design production RAG architecture (1 hour)
Behavioral: Past ML projects, debugging stories (30 min)

Backend Engineer:

Take-home: Build REST API for vector search (3 hours)
Technical: API design and optimization (1 hour)
System design: Scale to 1M requests/day (1 hour)
Behavioral: Production incidents, debugging (30 min)

Data Engineer:

Take-home: Build document processing pipeline (3 hours)
Technical: SQL and data modeling (1 hour)
System design: ETL for 1M documents/day (1 hour)
Behavioral: Data quality issues, debugging (30 min)

Training & Upskilling

For Existing Teams

Backend Engineers → RAG Engineers:

Week 1-2: LLM fundamentals (Coursera, fast.ai)
Week 3-4: Vector databases (Pinecone tutorials)
Week 5-6: Build simple RAG system
Week 7-8: Evaluation and optimization

Data Engineers → RAG Engineers:

Week 1-2: Embedding models (sentence-transformers)
Week 3-4: Vector search concepts
Week 5-6: Document chunking strategies
Week 7-8: Production data pipelines

Recommended Resources

Courses:

Books:

"Designing Machine Learning Systems" by Chip Huyen
"Building LLM Applications" by Valentina Alto

Communities:

Organizational Structure

Centralized AI Team

Pros:

Deep expertise concentration
Easier knowledge sharing
Consistent standards

Cons:

Can become bottleneck
Disconnect from product teams
Slower iteration

Best for: Early-stage, <10 engineers

Embedded AI Engineers

Pros:

Faster product iteration
Better product context
Distributed ownership

Cons:

Knowledge silos
Inconsistent practices
Harder to hire

Best for: Scale-stage, >20 engineers

Hybrid Model (Recommended)

Central AI Platform Team: 3-4 engineers building shared infrastructure
Embedded AI Engineers: 1-2 per product team using the platform

Compensation Benchmarks (2024, US)

Role	Junior	Mid	Senior	Staff
ML Engineer	$120k-150k	$160k-220k	$220k-300k	$300k-450k
Backend Engineer	$100k-130k	$140k-180k	$180k-240k	$240k-350k
Data Engineer	$110k-140k	$150k-190k	$190k-250k	$250k-370k

Note: Add 20-30% for SF Bay Area, subtract 20-30% for remote/international

Contractor vs Full-Time

Use Contractors For:

MVP development (3-6 months)
Specialized tasks (fine-tuning, evaluation setup)
Peak capacity (data labeling, testing)

Hire Full-Time For:

Core platform (long-term maintenance)
Production systems (on-call, reliability)
Strategic projects (competitive advantage)

Success Metrics for Teams

Velocity Metrics

Time to production: <3 months for MVP
Feature delivery: 2-3 major features/quarter
Bug fix time: <48 hours for critical, <1 week for minor

Quality Metrics

Retrieval accuracy: >90% Recall@5
System uptime: >99.9%
P95 latency: <2 seconds
Cost per query: <$0.01

Team Health

Retention rate: >90% annually
Satisfaction score: >4/5
On-call burden: <2 incidents/week
Knowledge sharing: 1 tech talk/month

Common Pitfalls

❌ Hiring Only ML Experts

Problem: Neglecting backend/data engineering leads to poor production systems

Solution: Balance team with strong backend and data engineers

❌ Underestimating Data Work

Problem: 60% of RAG effort is data processing, not ML

Solution: Hire data engineers early, invest in pipelines

❌ No Clear Ownership

Problem: Everyone's responsible = no one's responsible

Solution: Assign clear DRI (Directly Responsible Individual) for each component

❌ Ignoring On-Call

Problem: Production issues burn out team

Solution: Plan for on-call rotation from day 1

Next Steps

Decision Framework - Decide if RAG is right
Vendor Evaluation - Choose your stack
Getting Started - Technical implementation