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

Miguel Romero
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9 years of experience
Full-Time

Miguel is an AI specialist working on smart systems that improve user experiences.

Skills
  • Machine Learning
  • AI Strategy
  • Product Roadmapping
  • Data Modeling
  • Problem Solving
Óscar Alfaro
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11 years of experience
Full-Time

Óscar is an AI thinker designing adaptive systems with practical, scalable use.

Skills
  • AI Systems
  • Model Optimization
  • Neural Networks
  • Tech Innovation
  • Use Case Analysis
María González
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5 years of experience
Full-Time

María is an AI professional focused on developing innovative and practical tech solutions.

Skills
  • AI Strategy
  • Machine Learning
  • Product Roadmapping
  • Data Analysis
  • Problem Solving
Daniel Ospina
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7 years of experience
Full-Time

Daniel is an AI specialist crafting intelligent systems with practical user value.

Skills
  • Model Training
  • AI Architecture
  • Data Engineering
  • API Integration
  • AI Product Development
Luis Alfredo Torres
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10 years of experience
Full-Time

Luis Alfredo is an AI enthusiast who develops scalable and functional tech solutions.

Skills
  • AI Development
  • Machine Learning
  • System Design
  • Data Integration
  • Product Strategy
Esteban Aguirre
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5 years of experience
Full-Time

Esteban is an AI architect creating smart tools that solve complex, lasting challenges.

Skills
  • AI Infrastructure
  • Model Engineering
  • System Integration
  • Solution Design
  • Data Pipelines
Alexa Montiel
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4 years of experience
Full-Time

Alexa is an AI innovator applying intelligence to improve user experience.

Skills
  • ML Development
  • AI Applications
  • Predictive Modeling
  • Process Automation
  • UX Integration
Renata Figueroa
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4 years of experience
Full-Time

Renata is an AI expert turning innovation into intelligent, people-focused systems.

Skills
  • AI Systems
  • Data Engineering
  • Predictive Modeling
  • API Integration
  • Tech Strategy
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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”

Phillip Gutheim
Head of Product, Rappi Bank

“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”

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Joaquin Oliva
Co-Founder, EBI

"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."

Kim Heger
Chief Talent Officer, Hakkoda

"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."

Josh Berzansky
CEO, Proven Promotions & Vorgee USA

“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.”

Jeannine LeBeau
Director of People and Operations, Intevity

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!

Mike Bohlander
CTO and Co-Founder, Outgo

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.

Matt Clifford
Founder, Matt B. Clifford Consulting

LLM Engineers Soft Skills

LLM intuition and model handling strengths that guide natural language systems

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 tuning and retrieval integration that enable smarter AI pipelines

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

This is an example job post, including a sample salary expectation. Customize it to better suit your needs, budget, and attract top candidates.

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
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From search to hire, our process is designed to secure the perfect talent for your team

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Joseph Burns
Founder

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

Frequently Asked Questions

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