CV Template
AI Engineer CV Template: Showcase Machine Learning Models, LLM Deployments & Production Systems
An AI Engineer CV must demonstrate hands-on expertise in model development, deployment pipelines, and production-scale systems—not just theoretical knowledge. This template guides you to highlight quantified ML achievements, specific frameworks you've shipped, and infrastructure decisions that reduced latency or improved accuracy.
Written & reviewed by the CVWon Editorial Team · Updated June 2026
Build Your CV NowTo write a strong AI Engineer CV, lead with Professional Summary / AI Engineering Focus, Technical Skills (Organized by Category) and Experience (with Quantified Model & System Metrics) — each backed by specific, quantified results rather than generic duties. A strong AI Engineer CV balances theoretical depth with shipped systems.
ATS Optimisation
ATS Keywords
Include these keywords in your CV to pass applicant tracking systems.
A strong AI Engineer CV balances theoretical depth with shipped systems. Include specific model architectures you've built (e.g., 'fine-tuned BERT on domain-specific corpus achieving 94.2% F1'), quantified inference improvements ('reduced latency from 850ms to 120ms via quantization'), and production infrastructure (MLOps pipelines, monitoring, A/B testing frameworks). Name exact libraries and versions where relevant. Avoid vague claims like 'worked on AI projects'—instead specify model size, dataset scale, hardware used (A100 GPUs, TPU v4), and business impact (revenue lift, cost savings, user engagement metrics).
Structure
What Sections Should an AI Engineer CV Include?
Professional Summary / AI Engineering Focus
Recruiters scan the first 6 seconds; you must immediately signal your specialization (LLM inference, computer vision, reinforcement learning) and your scale of impact. Generic 'AI enthusiast' language kills your ATS score.
Example
ML Engineer with 4 years shipping production models at scale. Specialized in LLM fine-tuning and inference optimization; deployed 3 GPT-3.5 based systems serving 2M+ monthly requests with <200ms p95 latency. Proficient in PyTorch, ONNX quantization, and Kubernetes-based MLOps.
Technical Skills (Organized by Category)
AI Engineer hiring managers expect skills grouped by domain (frameworks, infrastructure, methodologies). Flat lists fail ATS parsing and confuse human readers about your depth.
Example
ML Frameworks: PyTorch, TensorFlow 2.x, JAX | LLM Tools: Hugging Face Transformers, LangChain, vLLM, OpenAI API | Infrastructure: CUDA, cuDNN, Docker, Kubernetes, Ray Cluster | Optimization: ONNX, TensorRT, Quantization (INT8, FP8), Pruning | Databases: Pinecone, Weaviate, Milvus (vector search)
Experience (with Quantified Model & System Metrics)
AI Engineer roles are judged on model performance (accuracy, latency, throughput) and production reliability. Vague descriptions ('improved model') don't differentiate you; metrics do.
Example
Deployed fine-tuned Llama-2-7B for customer support chatbot using QLoRA, reducing inference cost by 65% vs. GPT-4 API while maintaining 91% user satisfaction. Implemented RAG pipeline with Pinecone vector DB, improving answer relevance from 78% to 94% BLEU score. Optimized model serving with vLLM, achieving 4.2x throughput increase (120→500 req/s) on single A100.
Projects / Portfolio (with Architecture & Results)
AI Engineer portfolios prove you can architect end-to-end systems, not just train models. Include data pipeline, model choice rationale, and production constraints you solved.
Example
Computer Vision Pipeline for Defect Detection: Built YOLOv8-based inspection system processing 500 images/hour on edge devices (Jetson Orin). Trained on 50K annotated images, achieved 96.8% mAP. Deployed via ONNX + TensorRT, reducing inference time to 45ms per image. Reduced false positives by 34% vs. legacy rule-based system, saving $200K annually in rework costs.
Certifications & Continuous Learning
AI moves fast; certifications signal you stay current with production best practices, not just academic theory. Relevant certs boost ATS matching.
Example
AWS Certified Machine Learning – Specialty | DeepLearning.AI: LLM Ops & MLOps Engineering | Hugging Face: Efficient NLP Course | NVIDIA: CUDA Programming Fundamentals
Avoid These
What Are Common AI Engineer CV Mistakes?
FAQ
Frequently Asked Questions
Include Kaggle only if you placed top 10% or built a novel technique you later shipped in production. Recruiters prioritize shipped systems over competition rankings. If you have limited production experience, frame Kaggle as 'proof of concept' for a technique you later deployed (e.g., 'Kaggle 3rd place in time-series forecasting; applied winning ensemble approach to reduce inventory forecasting MAPE by 8% in production').
Specify the base model, dataset size, technique (LoRA, QLoRA, full fine-tune), and the metric improvement. Example: 'Fine-tuned Mistral-7B on 50K domain-specific Q&A pairs using QLoRA (rank=16, alpha=32), improving exact-match accuracy from 71% to 89% on internal benchmark. Reduced training time by 70% vs. full fine-tune while maintaining 98% of performance.' This shows you understand parameter efficiency and trade-offs.
MLOps (model monitoring, retraining pipelines, A/B testing, versioning) is increasingly critical for AI Engineer roles because it bridges research and production. List both: ML skills show you can build models; MLOps skills show you can keep them working at scale. Example: 'Built ML pipeline: data ingestion → training (PyTorch) → evaluation → deployment (Docker/K8s) → monitoring (Prometheus/Grafana) with automated retraining triggered by >2% accuracy drift.'
Yes—hardware knowledge directly impacts hiring for AI roles. Mention specific GPUs, TPUs, or edge devices you've optimized for: 'Optimized BERT inference for NVIDIA A100 and Jetson Orin using TensorRT, achieving 8.2x speedup on A100 and 2.1x on edge device.' Also mention distributed training: 'Scaled training across 16 A100 GPUs using PyTorch DDP, reducing training time from 72 hours to 5.5 hours.' This proves you understand hardware constraints.
Use relative improvements, industry benchmarks, or anonymized metrics. Instead of 'improved recommendation accuracy,' say 'increased NDCG@10 from 0.82 to 0.91 (+11%)' or 'reduced inference latency by 65% via quantization while maintaining <1% accuracy loss.' If data is proprietary, reference public benchmarks: 'Achieved 94.2% accuracy on internal dataset (comparable to GLUE benchmark performance for similar task scale).'
Salary
Salary by Experience Level
Typical salary ranges by seniority (EUR, gross).
| Level | Experience | Salary range |
|---|---|---|
| Entry Level | 0–2 years | €35K – €55K |
| Mid Level | 3–5 years | €55K – €85K |
| Senior Level | 6–10 years | €85K – €130K |
| Lead / Manager | 10+ years | €120K – €170K |
Download this AI Engineer CV template now and customize it with your model architectures, inference optimizations, and production metrics. Use our ATS checker to ensure your technical keywords rank for recruiter searches.
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