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
Luis is an AI generalist creating functional systems with real-world applications.
- AI Strategy
- Machine Learning
- System Design
- Product Roadmapping
- Problem Solving
Luis Alfredo is an AI enthusiast who develops scalable and functional tech solutions.
- AI Development
- Machine Learning
- System Design
- Data Integration
- Product Strategy
Matías is a skilled prompt engineer, adept at crafting precise and impactful AI interactions.
- Python
- AI Ethics
- Data Labeling
- NLP
- LLMs
Javier is an AI expert creating intelligent solutions that improve digital workflows.
- AI Strategy
- Machine Learning
- Product Roadmapping
- Data Modeling
- Problem Solving
Andrés is a skilled prompt engineer excelling in innovative solutions and creative problem-solving.
- Python
- LLMs
- AI Ethics
- Data Labeling
- NLP
Bárbara is an AI generalist building solutions that balance logic and human context.
- AI Tools
- Model Training
- ML Pipelines
- Prototyping
- Data Preprocessing
Renata is an AI expert turning innovation into intelligent, people-focused systems.
- AI Systems
- Data Engineering
- Predictive Modeling
- API Integration
- Tech Strategy
"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”
Lupa's Proven Process
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.
Reviews
"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”
“We scaled our first tech team at record speed with Lupa. We couldn’t be happier with the service and the candidates we were sent.”
"Recruiting used to be a challenge, but Lupa transformed everything. Their professional, agile team delivers top-quality candidates, understands our needs, and provides exceptional personalized service. Highly recommended!"
“Lupa has become more than just a provider; it’s a true ally for Pirani in recruitment processes. The team is always available to support and deliver the best service. Additionally, I believe they offer highly competitive rates and service within the market.”
"Highly professional, patient with our changes, and always maintaining clear communication with candidates. We look forward to continuing to work with you on all our future roles."
“Lupa has been an exceptional partner this year, deeply committed to understanding our unique needs and staying flexible to support us. We're excited to continue our collaboration into 2025.”
"What I love about Lupa is their approach to sharing small, carefully selected batches of candidates. They focus on sending only the three most qualified individuals, which has already helped us successfully fill 7 roles.”
"We hired 2 of our key initial developers with Lupa. The consultation was very helpful, the candidates were great and the process has been super fluid. We're already planning to do our next batch of hiring with Lupa. 5 stars."
"Working with Lupa for LatAm hiring has been fantastic. They found us a highly skilled candidate at a better rate than our previous staffing company. The fit is perfect, and we’re excited to collaborate on more roles."
"We compared Lupa with another LatAm headhunter we found through Google, and Lupa delivered a far superior experience. Their consultative approach stood out, and the quality of their candidates was superior. I've hired through Lupa for both of my companies and look forward to building more of my LatAm team with their support."
“We’ve worked with Lupa on multiple roles, and they’ve delivered time and again. From sourcing an incredible Senior FullStack Developer to supporting our broader hiring needs, their team has been proactive, kind, and incredibly easy to work with. It really feels like we’ve gained a trusted partner in hiring.”
Working with Lupa was a great experience. We struggled to find software engineers with a specific skill set in the US, but Lupa helped us refine the role and articulate our needs. Their strategic approach made all the difference in finding the right person. Highly recommend!
Lupa goes beyond typical headhunters. They helped me craft the role, refine the interview process, and even navigate international payroll. I felt truly supported—and I’m thrilled with the person I hired. What stood out most was their responsiveness and the thoughtful, consultative approach they brought.
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.
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.
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.
Book a Free ConsultationWhy We Stand Out From Other Recruiting Firms
From search to hire, our process is designed to secure the perfect talent for your team
Local Expertise
Tap into our knowledge of the LatAm market to secure the best talent at competitive, local rates. We know where to look, who to hire, and how to meet your needs precisely.
Direct Control
Retain complete control over your hiring process. With our strategic insights, you’ll know exactly where to find top talent, who to hire, and what to offer for a perfect match.
Seamless Compliance
We manage contracts, tax laws, and labor regulations, offering a worry-free recruitment experience tailored to your business needs, free of hidden costs and surprises.
Lupa will help you hire top talent in Latin America.
Book a Free Consultation
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
