Hire LLM Engineers
Connect with top LLM Engineers from Latin America. Skilled in fine-tuning, embeddings, and retrieval pipelines with remote setup in just 21 days.
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Hire Remote LLM Engineers
Rocío is an AI engineer creating smart tools that enhance digital products and systems.
- AI Research
- Natural Language Processing
- Data Analysis
- Cloud AI Services
- Project Execution
Natalia is an AI expert developing scalable, reliable, and human-friendly solutions.
- AI Strategy
- Machine Learning
- Neural Networks
- Product Development
- Data Science
Facundo is a dynamic AI researcher known for innovative solutions and insightful analysis.
- TensorFlow
- NLP
- Reinforcement Learning
- Computer Vision
- Deep Learning
Mariela is an AI innovator building technology with real, people-focused applications.
- AI Product Vision
- Model Integration
- Agile Prototyping
- Team Collaboration
- Ethical AI Design
Laura is a visionary AI researcher known for her innovative and insightful contributions.
- Computer Vision
- TensorFlow
- NLP
- Deep Learning
- Reinforcement Learning
Óscar is an AI thinker designing adaptive systems with practical, scalable use.
- AI Systems
- Model Optimization
- Neural Networks
- Tech Innovation
- Use Case Analysis
Estefanía is an AI professional creating adaptive systems that improve over time.
- AI Prototyping
- Model Evaluation
- Automated Systems
- ML Deployment
- Data Analysis
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"We came to Lupa Hire with a need to hire key tech and AI positions in Latin America. Our target when working with them was to find the best of the best in the region and they delivered. Their approach goes beyond what you'd expect from a headhunter with an incredible focus on match quality."
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
"What I love about Lupa Hire 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 20+ roles.”
"Talking about Lupa Hire, I would say: these are the people you want to work with. They understand what consultancies are like. They understand that they could work for a month on a req, only to have it pulled because a client contract didn’t go through. You understand our business model, and that is invaluable."
"We came to Lupa Hire with a need to hire key tech and AI positions in Latin America. Our target when working with them was to find the best of the best in the region and they delivered. Their approach goes beyond what you'd expect from a headhunter with an incredible focus on match quality."
"Before Lupa Hire, we struggled to find a vendor who could match both the volume and the quality we needed to build out our senior development team in Mexico. Lupa gave us real visibility into the pipeline and consistently sent us candidates who actually fit, not just resumes, but people who understood product engineering. It's made a real difference in how fast we've been able to grow the team."
“With Lupa Hire, we rebuilt our entire tech team in less than a month. We’re spending half as much on talent. Ten out of ten”
"We hired 2 of our key initial developers with Lupa Hire. 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."
“We’ve worked with Lupa Hire 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.”
"Lupa Hire 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."
"What Growth99 loves about working with Lupa Hire is how well they understand our needs. They don't just fill one role; they help us build out a whole team that fits together."
“The quality of candidates is great. Your pricing is reasonable. People are great to work with. I can't imagine a better experience.”
LLM Engineers Soft Skills
Critical Thinking
Evaluate trade-offs in prompt design and model tuning.
Documentation
Clearly log test results, changes, and versioning logic.
Communication
Translate LLM behavior into product-relevant terms.
Adaptability
Work with evolving APIs and language model formats.
Precision
Refine prompt inputs to control unpredictable outputs.
Problem Solving
Debug and optimize retrieval and generation pipelines.
LLM Engineers Skills
LLM Fine-Tuning
Adapt large models to specific domains or tasks.
Embedding & Vector Search
Implement vector-based retrieval with tools like FAISS.
Prompt Optimization
Refine prompt structures for precision and stability.
Token Management
Control token limits for performance and coherence.
LLM Toolchains
Work with LangChain, LlamaIndex, and Hugging Face.
Pipeline Design
Build systems combining LLMs, memory, and APIs.
How to Write an Effective Job Post to Hire LLM Engineers
Recommended Titles
- Large Language Model Engineer
- LLM Application Developer
- NLP Engineer
- Prompt Engineering Specialist
- Transformer Model Engineer
- LLM Integration Engineer
Role Overview
- Tech Stack: Proficient in LLM APIs (OpenAI, Cohere), vector DBs, Python, and LangChain.
- Project Scope: Build scalable applications powered by large language models and retrieval systems.
- Team Size: Contribute to LLM squads of 3–5 developers with MLOps support.
Role Requirements
- Years of Experience: 2–3 years with LLM-based or NLP-heavy applications.
- Core Skills: Context window management, token optimization, retrieval pipelines.
- Must-Have Technologies: LangChain, Pinecone, FAISS, OpenAI, FastAPI.
Role Benefits
- Salary Range: $100,000 – $160,000 based on LLM depth and product ownership.
- Remote Options: Remote with availability overlap for sync meetings.
- Growth Opportunities: Join fast-growing LLM deployments in SaaS, enterprise, or healthcare.
Do
- Specify expertise in training and fine-tuning LLMs
- Mention frameworks like Hugging Face, LangChain, or Transformers
- Include work with prompt optimization and model evaluation
- Highlight innovation in language model applications
- Use precise, AI-native terminology
Don't
- Don’t confuse general NLP roles with LLM specialization
- Avoid ignoring Hugging Face, LangChain, or Transformers
- Don’t overlook fine-tuning or prompt engineering
- Refrain from listing irrelevant AI tools or stacks
- Don’t skip real-world use case alignment
Top LLM Engineer Interview Questions
What to ask when hiring LLM Engineers
What’s your experience fine-tuning large language models?
Expect use of Hugging Face, LoRA, or PEFT techniques. Look for clarity on dataset prep and training configs.
How do you handle long context input limitations?
Look for strategies like chunking, retrieval-augmented generation, and summarization workflows.
What techniques do you use to improve output accuracy?
Expect prompt chaining, function calling, reranking outputs, or integrating structured data sources.
How do you monitor LLM performance in production?
Strong candidates mention evaluation sets, feedback loops, prompt testing tools, and observability metrics.
Can you describe how you’ve optimized LLM inference?
Look for batching, quantization, caching, or use of managed services like OpenAI or AWS Bedrock.
How do you approach fine-tuning when data is limited?
Look for use of LoRA, prompt tuning, synthetic data generation, and transfer learning strategies.
Describe a time you debugged unexpected outputs in a custom LLM.
Expect prompt audits, dataset validation, and output sampling to trace model behavior.
How do you handle prompt injection vulnerabilities?
Expect sanitization, user input isolation, or system prompt reinforcement techniques.
What’s your strategy when inference latency becomes unacceptable?
Expect model quantization, caching, or moving to optimized runtimes like ONNX or Hugging Face Transformers.
How do you troubleshoot alignment issues in generation?
Look for use of reward modeling, preference learning, or instruction tuning to guide behavior.
Tell me about a time you customized an LLM for a unique use case.
Look for prompt design rigor, evaluation loop setup, and domain alignment.
Describe how you navigate tension between latency and performance in LLM deployments.
Expect experience with batching, caching, and infrastructure trade-offs.
How do you communicate LLM limitations to business stakeholders?
Look for clear examples, risk flags, and alignment with use case constraints.
What’s your approach to team feedback on prompt design experiments?
Expect openness, structured experimentation, and cross-functional collaboration.
Have you ever faced resistance to integrating LLMs into existing workflows?
Expect empathy, phased rollout, and measurable outcome framing.
- Inability to fine-tune or prompt large models effectively
- Overlooks context management and token limits
- Limited experience with embedding-based search
- Fails to align model behavior with user intent
- Over-reliance on default API behaviors
Build elite teams in record time, full setup in 21 days or less.
Book a Consultation CallWhy 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 Consultation Call
I help companies hire exceptional talent in Latin America. My journey took me from growing up in a small town in Ohio to building teams at Capital One, Meta, and eventually Rappi, for which I moved from Silicon Valley to Colombia and had to recruit a local tech team from scratch. That’s where I realized traditional recruiting was broken, and how much available potential there was in Latin American talent. Almost ten years later, I still work closely with Latin American professionals, both for my company and for clients. They know US business culture, speak great English, work in the same time zones, and bring strong skills and dedication at a better cost. We have helped companies like Rappi, Globant, Capital One, Google, and IBM build their teams with top talent from the region.
Top LLM Engineer Interview Questions
What to ask when hiring LLM Engineers
What’s your experience fine-tuning large language models?
Expect use of Hugging Face, LoRA, or PEFT techniques. Look for clarity on dataset prep and training configs.
How do you handle long context input limitations?
Look for strategies like chunking, retrieval-augmented generation, and summarization workflows.
What techniques do you use to improve output accuracy?
Expect prompt chaining, function calling, reranking outputs, or integrating structured data sources.
How do you monitor LLM performance in production?
Strong candidates mention evaluation sets, feedback loops, prompt testing tools, and observability metrics.
Can you describe how you’ve optimized LLM inference?
Look for batching, quantization, caching, or use of managed services like OpenAI or AWS Bedrock.
How do you approach fine-tuning when data is limited?
Look for use of LoRA, prompt tuning, synthetic data generation, and transfer learning strategies.
Describe a time you debugged unexpected outputs in a custom LLM.
Expect prompt audits, dataset validation, and output sampling to trace model behavior.
How do you handle prompt injection vulnerabilities?
Expect sanitization, user input isolation, or system prompt reinforcement techniques.
What’s your strategy when inference latency becomes unacceptable?
Expect model quantization, caching, or moving to optimized runtimes like ONNX or Hugging Face Transformers.
How do you troubleshoot alignment issues in generation?
Look for use of reward modeling, preference learning, or instruction tuning to guide behavior.
Tell me about a time you customized an LLM for a unique use case.
Look for prompt design rigor, evaluation loop setup, and domain alignment.
Describe how you navigate tension between latency and performance in LLM deployments.
Expect experience with batching, caching, and infrastructure trade-offs.
How do you communicate LLM limitations to business stakeholders?
Look for clear examples, risk flags, and alignment with use case constraints.
What’s your approach to team feedback on prompt design experiments?
Expect openness, structured experimentation, and cross-functional collaboration.
Have you ever faced resistance to integrating LLMs into existing workflows?
Expect empathy, phased rollout, and measurable outcome framing.
- Inability to fine-tune or prompt large models effectively
- Overlooks context management and token limits
- Limited experience with embedding-based search
- Fails to align model behavior with user intent
- Over-reliance on default API behaviors
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