Hire RAG Developers
Tap into RAG Developers from Latin America. Skilled in retrieval pipelines, vector databases, and LLM integration with remote team setup in just 21 days.
Hire Remote RAG Developers
Ernesto is an AI developer focused on exploring and building smart technical systems.
- Machine Learning
- AI Engineering
- Algorithm Design
- Data Processing
- Prototyping
Paola is an AI developer building useful, intelligent products for real applications.
- Machine Learning
- Neural Networks
- Python Development
- Model Deployment
- Tech Integration
Josefina is an AI specialist building intelligent systems with practical human benefits.
- AI Roadmapping
- Applied Machine Learning
- Data Strategy
- Feature Engineering
- Model Testing
Estefanía is an AI professional creating adaptive systems that improve over time.
- AI Prototyping
- Model Evaluation
- Automated Systems
- ML Deployment
- Data Analysis
Esteban is an AI architect creating smart tools that solve complex, lasting challenges.
- AI Infrastructure
- Model Engineering
- System Integration
- Solution Design
- Data Pipelines
"Over the course of 2024, we successfully hired 9 exceptional team members through Lupa, spanning mid-level to senior roles. The quality of talent has been outstanding, and we’ve been able to achieve payroll cost savings while bringing great professionals onto our team. We're very happy with the consultation and attention they've provided us."
“We needed to scale a new team quickly - with top talent. Lupa helped us build a great process, delivered great candidates quickly, and had impeccable service”
“With Lupa, we rebuilt our entire tech team in less than a month. We’re spending half as much on talent. Ten out of ten”
RAG Developers Skills
Vector Database Integration
Connect LLMs to Pinecone, Weaviate, or similar tools.
Document Chunking
Split content into retrievable segments for accuracy.
Embedding Models
Generate embeddings using OpenAI, Cohere, or Hugging Face.
Context Injection
Pass relevant content into prompts to guide model output.
Search Ranking Logic
Rank retrieved results based on relevance scoring.
End-to-End Pipelines
Design retrieval + generation flows for enterprise search.
RAG Developers Soft Skills
Systems Thinking
Connect LLMs, search, and logic into cohesive solutions.
Problem Solving
Troubleshoot retrieval errors and model hallucinations.
Attention to Detail
Fine-tune context flow for high-output relevance.
Curiosity
Explore hybrid architectures for retrieval+generation.
Team Collaboration
Work across backend, ML, and product functions.
Adaptability
Handle evolving libraries, APIs, and data formats.
How to Hire RAG Developers with Lupa
Build retrieval-augmented generation pipelines with expert RAG Developers. Hire through our Tech Recruiting Agency, scale with Remote Staffing Services, or embed talent via our RPO solutions.
Together, we'll create a precise hiring plan, defining your ideal candidate profile, team needs, compensation and cultural fit.
Our tech-enabled search scans thousands of candidates across LatAm, both active and passive. We leverage advanced tools and regional expertise to build a comprehensive talent pool.
We carefully assess 30+ candidates with proven track records. Our rigorous evaluation ensures each professional brings relevant experience from industry-leading companies, aligned to your needs.
Receive a curated selection of 3-4 top candidates with comprehensive profiles. Each includes proven background, key achievements, and expectations—enabling informed hiring decisions.
Top candidates ready for your assessment. We handle interview logistics and feedback collection—ensuring smooth evaluation. Not fully convinced? We iterate until you find the perfect fit.
We manage contracting, onboarding, and payment to your team seamlessly. Our partnership extends beyond hiring—providing retention support and strategic guidance for the long-term growth of your LatAm team.
How to Write an Effective Job Post to Hire RAG Developers
Recommended Titles
- Retrieval-Augmented Generation Engineer
- RAG Application Developer
- LLM Retrieval Developer
- Knowledge-Augmented NLP Engineer
- Document Search & Generation Specialist
- RAG Pipeline Engineer
Role Overview
- Tech Stack: Skilled in LangChain, Pinecone, OpenAI APIs, and Python.
- Project Scope: Build retrieval-augmented generation systems for LLM-powered apps.
- Team Size: Collaborate with 3–5 engineers focused on NLP and vector search.
Role Requirements
- Years of Experience: 2–3 years working with LLMs or semantic search tools.
- Core Skills: Document retrieval, embedding generation, indexing, and query optimization.
- Must-Have Technologies: LangChain, FAISS, Pinecone, OpenAI, Python.
Role Benefits
- Salary Range: $100,000 – $160,000 depending on depth of RAG implementation experience.
- Remote Options: Fully remote with flexible scheduling across time zones.
- Growth Opportunities: Build foundational systems for enterprise-grade AI products.
Do
- Include experience with Retrieval-Augmented Generation pipelines
- Mention tools like LangChain, Pinecone, or Weaviate
- List integration of vector databases and LLMs
- Highlight use cases in enterprise knowledge systems
- Use structured, NLP-aware job descriptions
Don't
- Don’t generalize LLM roles without referencing retrieval logic
- Avoid omitting vector database or semantic search skills
- Don’t post without clarity on framework/tooling (e.g., LangChain)
- Refrain from vague mentions of “AI integration”
- Don’t ignore context handling or data quality alignment
Top RAG Developer Interview Questions
Questions to ask RAG Developers in technical interviews
How do you implement Retrieval-Augmented Generation in production?
Look for explanation of retrievers, vector databases, and prompt construction. Bonus if they’ve used LangChain or similar orchestration frameworks.
What embedding models have you worked with?
Expect OpenAI, SentenceTransformers, Cohere, or custom-trained models. Look for understanding of trade-offs in vector quality and size.
How do you manage document chunking and context windows?
They should discuss chunk size tuning, overlap strategies, and optimizing for retrieval quality.
What vector database platforms have you used?
Look for Pinecone, Weaviate, FAISS, or Qdrant. Bonus if they can discuss indexing, filtering, and scaling.
How do you evaluate the quality of RAG responses?
Expect mention of grounding checks, factual consistency, retrieval accuracy, and latency benchmarks.
How do you debug hallucinations in a RAG system?
Look for inspection of retrieval quality, prompt clarity, and source document alignment with generated output.
What’s your approach when retrieval returns irrelevant documents?
Expect query reformulation, vector database tuning, re-ranking strategies, and improved chunking techniques.
Describe how you monitor the relevance of RAG responses in production.
Expect mention of human feedback loops, embeddings drift detection, or semantic similarity scoring.
How do you troubleshoot latency in large-scale RAG pipelines?
Look for batched queries, optimized embeddings, and distributed retrieval systems.
What do you do when generation contradicts retrieved sources?
Expect use of grounding logic, answer masking, or formatting that highlights original source evidence.
Describe a time you collaborated across teams to ship a model to production.
Expect coordination with MLOps, product managers, and devs—plus ownership of handoff success.
Tell me about a time your deployment failed—what did you learn?
Look for ownership, alerting improvements, and resilience through iteration.
How do you handle tension between research and engineering constraints?
Expect pragmatism, backlog balancing, and milestone planning.
Describe how you respond when metrics stop improving.
Expect root cause analysis, feature engineering pivots, or label strategy reviews.
How do you manage technical debt in ML pipelines?
Look for scheduled cleanup, modular design, and team-wide knowledge sharing.
- Misunderstands vector search performance trade-offs
- Fails to optimize retrieval for domain-specific needs
- Uses generic chunking/tokenization strategies blindly
- Weak grasp of latency and scoring mechanisms
- Overlooking fallback strategies for poor results
Build elite teams in record time, full setup in 21 days or less.
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