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LLM Engineer — CV Example

Build AI applications with fine-tuning, RAG, and model optimization expertise

What Does a LLM Engineer Actually Do?

LLM Engineers design, deploy, and optimize large language model systems at scale. Working alongside ML teams at companies like OpenAI, Anthropic, Google, and Stripe, they focus on fine-tuning models, implementing retrieval-augmented generation (RAG), optimizing prompts, managing vector databases, and evaluating model performance. Day-to-day involves writing Python code, working with APIs (OpenAI, Anthropic, Hugging Face), conducting experiments with prompt engineering, and ensuring production systems handle millions of inferences reliably.

Alex Chen

LLM Engineer

📍 London, UK✉️ alex.chen@email.com

Summary

LLM Engineer with 4+ years scaling language model systems at OpenAI and Anthropic. Expert in RAG architecture, fine-tuning, and prompt optimization, delivering 40% latency reduction and 35% cost savings across production systems. Passionate about building next-generation AI applications with measurable business impact.

Work Experience

Senior LLM Engineer at OpenAISep 2023 — Present

Architected and deployed RAG pipeline processing 50K+ queries daily, achieving 28% improvement in retrieval accuracy.
Fine-tuned GPT-3.5 models on proprietary datasets, reducing inference latency by 40% while maintaining 99.2% accuracy.

LLM Engineer at AnthropicMar 2022 — Sep 2023

Implemented prompt engineering strategies across 8 production applications, improving response quality scores by 32%.
Built and maintained Claude API integrations for 12 enterprise clients, handling 2M+ API calls monthly at 99.95% uptime.

Skills

Fine-tuning (GPT-3.5, Claude, Llama 2)Retrieval Augmented Generation (RAG)Prompt Engineering & Few-shot LearningVector Databases (Pinecone, Weaviate, Milvus)OpenAI & Anthropic APIsLLM Evaluation FrameworksPython & PyTorchModel Optimization & QuantizationTransformer ArchitecturesLangChain & LlamaIndex

What Recruiters Look For

Recruiters want three things: First, hands-on experience with production LLMs (GPT, Claude, Llama) and APIs—not just theory. Second, quantified impact: latency improvements, cost reductions, or accuracy gains with numbers and percentages. Third, full-stack capability—fine-tuning to vector databases to prompt optimization. They also value evaluation rigor: candidates who measure performance, not guess. Experience with scaling (millions of inferences), monitoring, and A/B testing separates senior candidates. Research the company's model preferences beforehand—OpenAI candidates mention GPT experience, Anthropic emphasize Claude.

Key Skills to Include

Core technical: fine-tuning frameworks (LoRA, QLoRA), RAG systems, vector databases (Pinecone, Weaviate, Milvus), prompt engineering, and model evaluation. APIs: OpenAI, Anthropic, Hugging Face Inference. Languages: Python (essential), with PyTorch and TensorFlow experience. Tools: LangChain, LlamaIndex, ONNX, BitsandBytes for quantization. Soft skills matter too—communication (explaining model performance to non-technical stakeholders), documentation, and experimentation rigor. Don't list outdated skills; focus on models from 2023+ (GPT-3.5, GPT-4, Claude, Llama 2). Include specific model sizes you've worked with (fine-tuning 7B vs. 70B requires different expertise).

Common Mistakes

Mistake 1: Vague bullets like 'worked on LLM projects' with no metrics. Always include numbers. Mistake 2: Claiming expertise in 20 tools when depth matters more—pick the 8-10 you genuinely know. Mistake 3: Omitting evaluation rigor. Recruiters want to see you test, measure, and iterate, not just deploy. Mistake 4: Forgetting business impact. Saying 'optimized latency' is weak; 'reduced inference latency from 2.1s to 0.8s, improving user adoption by 22%' is strong. Mistake 5: Listing only recent work. Include earlier roles where you built ML foundations. Mistake 6: No mention of production experience—side projects matter less than scaling to real users at scale.

Formatting Tips

Use a clean, single-column layout with clear date formatting (Month Year). Lead each bullet with action verbs: Architected, Optimized, Deployed, Fine-tuned, Evaluated. Keep bullets to 12-20 words—recruiters scan in 6 seconds. Quantify everything: numbers, percentages, currency, latency (ms), or accuracy metrics. Group related experiences together logically by tech stack, not chronology. Use consistent date ranges and avoid employment gaps without context. Include a 2-3 sentence professional summary highlighting your strongest achievement. For skills, use 2-3 columns to save space. Add relevant GitHub links or portfolio projects showing LLM work. Bold company names and your job title for scannability.

Average Salary — LLM Engineer

United States

$165,000 – $280,000

United Kingdom

£120,000 – £200,000

Germany

€110,000 – €180,000

UAE / Dubai

$160,000 – $270,000

Canada

CAD $155,000 – CAD $260,000

Australia

AUD $180,000 – AUD $300,000

Figures in USD. Ranges reflect mid-level experience (3–7 years). Senior roles and major metro areas typically sit at the top of these bands.

Top 5 Interview Questions — LLM Engineer

1Walk us through how you'd implement a RAG pipeline from scratch. What vector database would you choose and why?

I'd start by defining the retrieval problem: document corpus size, query latency SLA, and accuracy requirements. For a 1M+ document corpus with sub-200ms latency needs, I'd use Pinecone for managed infrastructure or Weaviate for self-hosted control. Architecture includes document chunking (250-token chunks with overlap), embedding generation via OpenAI's text-embedding-3-small, vector indexing with HNSW, and retrieval evaluation using NDCG@10 metrics. I'd implement reranking with a cross-encoder for top-5 precision improvement, then integrate with LangChain for orchestration. Monitoring would track embedding quality drift and retrieval recall against ground truth datasets monthly.

2Describe a time you optimized LLM costs in production. What was your approach and results?

At my previous role, API costs were £8K monthly with GPT-4. I implemented a three-tier strategy: First, profiled token usage across 50 endpoints—found 30% waste in verbose prompts. Second, fine-tuned GPT-3.5 on domain-specific tasks (classification, summarization), reducing per-token cost 10x. Third, cached common context using prompt caching, cutting context tokens by 45%. Results: reduced monthly spend to £5.2K (35% savings), improved latency to 800ms from 2.1s, and maintained 99.2% accuracy. I automated cost tracking via custom dashboards.

3How do you evaluate whether a fine-tuned model is production-ready?

I use a comprehensive evaluation framework: First, accuracy metrics (BLEU, ROUGE, exact match) on held-out test sets of 500+ examples. Second, cost-benefit analysis—does the 2% accuracy gain justify 8x inference cost? Third, latency benchmarking across batch and real-time scenarios. Fourth, adversarial testing with 100+ edge cases to catch failure modes. Fifth, comparison against baseline models using paired t-tests for statistical significance. Finally, A/B testing in production with 5% traffic before full rollout. I document evaluation reports linking metrics to business KPIs (customer satisfaction, revenue impact).

4You notice your RAG system's retrieval accuracy dropped from 89% to 76% overnight. How do you debug?

I'd systematically isolate the issue: First, check if embeddings changed—verify embedding model version and date of last reindex (embedding drift is common). Second, query the vector database directly to confirm indexing isn't corrupted. Third, analyze retrieval queries against a sample of failures—perhaps new question patterns aren't in training distribution. Fourth, evaluate document quality by checking if new documents were added with poor chunking. Fifth, review retrieval metrics by document category to identify specific weak spots. Finally, implement monitoring dashboards tracking daily NDCG@10 and mean reciprocal rank to catch future drops within 2 hours, not overnight.

5What's your experience with model quantization, and when would you apply it?

Quantization reduces model size and latency by converting weights from float32 to int8 or int4, typically losing <2% accuracy. I've used ONNX quantization for edge deployment and bitsandbytes for 4-bit quantization on Llama 2 fine-tuning, reducing VRAM by 75%. I apply quantization when: device memory is constrained (mobile, edge), latency is critical (<100ms), or cost per inference is a KPI. For Llama 2 7B, int4 quantization achieved 3.2x speedup with 0.8% accuracy drop. However, I avoid it for reasoning-heavy tasks where nuance matters. I always measure accuracy loss empirically on domain-specific benchmarks before production.

How to Tailor Your CV

Target OpenAI, Anthropic, Google DeepMind, and Stripe—they aggressively hire LLM Engineers. For OpenAI roles, emphasize scale: mention processing millions of API calls, optimizing embeddings at 50M+ dimensions. Anthropic values safety-focused work and careful evaluation; highlight your model evaluation frameworks and testing rigor. Google prefers candidates experienced with Gemini or PaLM APIs plus ML infrastructure; showcase infrastructure work. Stripe wants LLM solutions improving fraud detection, customer support, or content moderation—quantify business impact. Tailor your CV showing relevant API experience and the specific model versions you've worked with.

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