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.
Hire Remote LLM Engineers
Miguel is an AI specialist working on smart systems that improve user experiences.
- Machine Learning
- AI Strategy
- Product Roadmapping
- Data Modeling
- Problem Solving
Óscar is an AI thinker designing adaptive systems with practical, scalable use.
- AI Systems
- Model Optimization
- Neural Networks
- Tech Innovation
- Use Case Analysis
María is an AI professional focused on developing innovative and practical tech solutions.
- AI Strategy
- Machine Learning
- Product Roadmapping
- Data Analysis
- Problem Solving
Daniel is an AI specialist crafting intelligent systems with practical user value.
- Model Training
- AI Architecture
- Data Engineering
- API Integration
- AI Product Development
Luis Alfredo is an AI enthusiast who develops scalable and functional tech solutions.
- AI Development
- Machine Learning
- System Design
- Data Integration
- Product Strategy
Esteban is an AI architect creating smart tools that solve complex, lasting challenges.
- AI Infrastructure
- Model Engineering
- System Integration
- Solution Design
- Data Pipelines
Alexa is an AI innovator applying intelligence to improve user experience.
- ML Development
- AI Applications
- Predictive Modeling
- Process Automation
- UX Integration
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“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.
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 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.
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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.
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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
