Hiring LLM Engineers: The Complete Guide

What LLM Engineers Actually Do

LLM engineering combines ML knowledge with software engineering to build production AI systems.

Retrieval-Augmented Generation (RAG)

Most production LLM applications use RAG:

• Embedding pipelines — Processing documents into vector representations
• Vector databases — Managing and querying embeddings (Pinecone, Weaviate, Qdrant)
• Retrieval strategies — Hybrid search, reranking, context window optimization
• Chunking strategies — Document segmentation for optimal retrieval
• Context assembly — Building prompts with retrieved information

Fine-Tuning & Customization

Adapting models for specific use cases:

• Dataset preparation — Curating training data, quality control
• Fine-tuning techniques — Full fine-tuning, LoRA, RLHF
• Evaluation — Measuring improvements, preventing regression
• Model selection — Choosing base models, trade-off analysis
• Deployment — Serving fine-tuned models efficiently

Production Systems

Building reliable LLM applications:

• API design — Designing LLM-powered APIs
• Latency optimization — Streaming, caching, model selection
• Cost management — Token optimization, model routing
• Monitoring — Quality tracking, drift detection
• Guardrails — Content filtering, output validation

Evaluation & Quality

Measuring LLM system quality:

• Benchmark design — Task-specific evaluation sets
• Automated evaluation — LLM-as-judge, similarity metrics
• Human evaluation — Annotation systems, quality assurance
• A/B testing — Comparing system variants
• Regression testing — Ensuring changes don't break functionality

LLM Engineer vs. Related Roles

LLM Engineer vs. ML Engineer

LLM Engineer vs. Prompt Engineer

LLM Engineer vs. AI Engineer

LLM Engineer is a specialization within AI Engineering. AI Engineers may work on various AI systems (computer vision, speech, recommendations), while LLM Engineers focus specifically on language model applications.

Skills by Experience Level

Junior LLM Engineer (0-2 years)

Capabilities:

• Build basic RAG systems with standard tools
• Fine-tune models using existing frameworks
• Implement evaluation metrics
• Work with vector databases
• Understand LLM behavior and limitations

Learning areas:

• Advanced retrieval strategies
• Production operations
• Cost and latency optimization
• Complex evaluation frameworks

Mid-Level LLM Engineer (2-4 years)

Capabilities:

• Design RAG systems for complex use cases
• Optimize retrieval and context assembly
• Build evaluation frameworks
• Make model selection decisions
• Handle production issues
• Mentor junior engineers

Growing toward:

• System architecture
• Strategic technology decisions
• Team leadership

Senior LLM Engineer (4+ years)

Capabilities:

• Architect LLM systems at scale
• Make build vs. buy decisions
• Define evaluation strategies
• Lead technical direction
• Bridge research and production
• Drive best practices

Interview Focus Areas

RAG Systems

Core competency for most roles:

• "Design a RAG system for [use case]. Walk me through your decisions."
• "How do you choose chunking strategies for different document types?"
• "How do you evaluate retrieval quality?"
• "What causes retrieval failures and how do you debug them?"

Fine-Tuning

For roles involving model customization:

• "When would you fine-tune vs. use prompting?"
• "Explain LoRA and when you'd use it"
• "How do you prepare and validate training data?"
• "How do you prevent overfitting in fine-tuning?"

Production Systems

Engineering fundamentals:

• "How do you handle latency in LLM applications?"
• "Design a caching strategy for an LLM API"
• "How do you monitor LLM quality in production?"
• "How do you handle cost optimization?"

Evaluation

Critical thinking about quality:

• "How do you evaluate whether an LLM system is working?"
• "When would you use LLM-as-judge vs. human evaluation?"
• "How do you handle subjective quality assessment?"
• "Design an evaluation framework for [use case]"

Common Hiring Mistakes

Conflating with ML Research

LLM engineers build applications, not research new architectures. Deep learning theory is less important than practical system building. Don't require PhD or research publications unless you're doing fundamental research.

Ignoring Software Engineering

LLM systems are software systems. Candidates need solid engineering skills: API design, testing, monitoring, production operations. Pure ML backgrounds without engineering rigor struggle with production systems.

Over-Specifying Tools

The ecosystem changes rapidly. Requiring specific vector databases or frameworks is less important than fundamental understanding. Strong engineers learn new tools quickly.

Expecting Stable Best Practices

The field evolves monthly. Hire for learning ability and first-principles thinking over specific techniques. Today's best practices may be obsolete in six months.

Where to Find LLM Engineers

High-Signal Sources

• AI communities — LangChain, LlamaIndex, Hugging Face communities
• Technical content — Bloggers writing about RAG, fine-tuning, LLM systems
• GitHub — Contributors to LLM frameworks and tools
• ML engineers — Those transitioning to LLM specialization
• daily.dev — AI-focused developers discussing LLM patterns

Background Transitions

Recruiter's Cheat Sheet

Resume Green Flags

• Production LLM system experience
• RAG system design and optimization
• Fine-tuning experience
• Evaluation framework development
• Software engineering skills alongside ML
• Experience with multiple LLM providers

Resume Yellow Flags

• Only API usage, no system building
• Pure research without production experience
• No evaluation or quality focus
• Prompt engineering only (different role)

Technical Terms to Know