Generative AI Engineer Resume Example
Generative AI Engineer hiring managers look for credible ownership of production GenAI systems: fine-tuning, RAG, and multimodal applications. Generative AI Engineer with 4+ years building production GenAI systems — LLM fine-tuning, RAG pipelines, multimodal applications, and AI infrastructure.
This sample shows how Python, PyTorch, Hugging Face Transformers, LoRA, QLoRA map to owned responsibilities—adapt every line to work you can defend in an interview.
Generative AI Engineer Resume Sample
Pooja Krishnan
Generative AI Engineer
Bengaluru, Karnataka · pooja.krishnan@email.com · +91-9900112244 · linkedin.com/in/pooja-genai · github.com/pooja-genai
Professional Summary
Generative AI Engineer with 4+ years building production GenAI systems — LLM fine-tuning, RAG pipelines, multimodal applications, and AI infrastructure. Shipped 8 GenAI products used by 2M+ users. Expert in LLM adaptation, evaluation, and cost-optimized serving using open-source and proprietary models.
Generative AI Engineer Technical Skills
Core Skills: Python · PyTorch · Hugging Face Transformers · LoRA · QLoRA · PEFT · vLLM · TGI · LangChain · LlamaIndex · OpenAI API · Anthropic API · Gemini API · RAG · Vector DBs (Qdrant, Pinecone) · RLHF · LangSmith · W&B · FastAPI · Docker · Kubernetes · AWS SageMaker
Professional Experience
- Fine-tuned Llama 3 70B for Indian legal document summarization using QLoRA + DPO — outperformed GPT-4o on in-domain benchmark at 8% of inference cost.
- Built production RAG pipeline for enterprise knowledge management serving 50K+ queries/day — hybrid BM25 + dense retrieval with reranking achieving 89% answer relevance.
- Designed multi-modal document understanding system (text + tables + charts) using Claude 3.5 + custom OCR pipeline — processing 10K+ financial reports/month for institutional client.
- Built LLM evaluation framework with 8 automated dimensions (accuracy, groundedness, safety, format, latency) + human review queue — reduced manual QA time by 70%.
- Deployed vLLM serving cluster for 4 fine-tuned models with PagedAttention and continuous batching — achieved 4x throughput vs naive single-request serving at same GPU count.
- Reduced average LLM inference cost by 55% through model tiering, prompt compression (LLMLingua), and aggressive caching strategy.
- Built GPT-3.5-based structured data extraction pipeline for financial reports — reduced analyst manual extraction time from 4 hours to 8 minutes per report.
- Developed prompt engineering framework with chain-of-thought templates for complex reasoning tasks — improved task accuracy from 61% to 84%.
- Created automated LLM regression test suite comparing outputs across model updates — catching 12 quality regressions before production rollout.
Generative AI Engineer Projects
Open-source benchmark evaluating LLMs (GPT-4o, Claude 3.5, Llama 3, Gemini) on Indian legal, financial, and cultural knowledge across Hindi, Tamil, and English — 800+ GitHub stars.
Education
M.Tech Artificial Intelligence — IIT Hyderabad, 2021 | CGPA: 9.0/10
Certifications
- DeepLearning.AI Generative AI with LLMs
- Hugging Face ML Course Certificate
- AWS Certified Machine Learning – Specialty
All details in this resume example are illustrative and should be replaced with your actual experience, achievements, education, and certifications.
Practical Generative AI Engineer resume guidance focused on production GenAI systems: fine-tuning, RAG, and multimodal applications, using only claims you can verify from your own history.
How to Write a Generative AI Engineer Resume
Interviewers need proof of production GenAI systems: fine-tuning, RAG, and multimodal applications, not an undifferentiated cloud of neighboring tools.
Ground depth in Python, PyTorch, Hugging Face Transformers, LoRA, QLoRA by linking each skill to a responsibility from your summary, experience, or indic-llm-bench.
Resumes stumble when they generic AI Engineer chatbot demos without production GenAI stack. Keep every technology claim tied to something you personally owned.
Prefer decision language—what you modeled, operated, secured, led, or shipped—over tool inventories that could fit any adjacent title.
Close the loop by showing how Transformers/LoRA/vLLM-class generative AI delivery appears in your bullets, projects, and summary without inventing employers, percentages, or scale.
Experienced professional skilled in many modern tools related to generative ai engineer.
Fine-tuned Llama 3 70B for Indian legal document summarization using QLoRA + DPO — outperformed GPT-4o on in-domain benchmark at 8% of inference cost.
What to Include in a Generative AI Engineer Resume
Cover Python, PyTorch, Hugging Face Transformers, LoRA, QLoRA, PEFT, vLLM, TGI when truthful, grouped the way you actually practiced the work rather than as a buzzword dump.
Add DeepLearning.AI Generative AI with LLMs, Hugging Face ML Course Certificate, or AWS Certified Machine Learning – Specialty only if completed, preserving official credential names.
Include indic-llm-bench with technologies such as Python, PyTorch, Hugging Face Transformers, LoRA when you need compact proof alongside employment bullets. Add a certifications subsection because this source includes DeepLearning.AI Generative AI with LLMs; Hugging Face ML Course Certificate; AWS Certified Machine Learning – Specialty; on your resume, list only credentials you actually hold and preserve their official names.
Generative AI Engineer Resume Summary Example
Begin with 4 years centered on production GenAI systems: fine-tuning, RAG, and multimodal applications, then reinforce the strongest theme already present in the professional summary.
Generative AI Engineer with 4+ years building production GenAI systems — LLM fine-tuning, RAG pipelines, multimodal applications, and AI infrastructure. Shipped 8 GenAI products used by 2M+ users. Expert in LLM adaptation, evaluation, and cost-optimized serving using open-source and proprietary models.
Important Generative AI Engineer Skills for a Resume
Core Skills
Python · PyTorch · Hugging Face Transformers · LoRA · QLoRA · PEFT · vLLM · TGI · LangChain · LlamaIndex · OpenAI API · Anthropic API · Gemini API · RAG · Vector DBs (Qdrant, Pinecone) · RLHF · LangSmith · W&B · FastAPI · Docker · Kubernetes · AWS SageMaker
Retain Generative AI Engineer skills you can defend with a delivery story, design choice, incident, test, leadership example, or project walkthrough.
Generative AI Engineer Resume Experience Examples
Senior Generative AI Engineer
Fine-tuned Llama 3 70B for Indian legal document summarization using QLoRA + DPO — outperformed GPT-4o on in-domain benchmark at 8% of inference cost.
Senior Generative AI Engineer
Deployed vLLM serving cluster for 4 fine-tuned models with PagedAttention and continuous batching — achieved 4x throughput vs naive single-request serving at same GPU count.
Senior Generative AI Engineer
Reduced average LLM inference cost by 55% through model tiering, prompt compression (LLMLingua), and aggressive caching strategy.
Senior Generative AI Engineer
Built production RAG pipeline for enterprise knowledge management serving 50K+ queries/day — hybrid BM25 + dense retrieval with reranking achieving 89% answer relevance.
Use real numbers when you can verify them. Do not invent metrics simply to make the resume sound stronger.
Generative AI Engineer ATS Keywords
Choose keywords that match both the Generative AI Engineer job description and work you can substantiate. Spell out important concepts naturally in summary and experience instead of pasting this list.
Generative AI Engineer Resume Tips
Lead with GenAI systems
Open with fine-tuning, RAG, or multimodal apps you shipped.
Show training/serving stack
Mention LoRA/QLoRA/PEFT, vLLM, or TGI only when used.
Place Hugging Face carefully
Include Transformers when that was your stack.
Differentiate from Prompt/GPT Integration roles
Emphasize model systems engineering, not only API glue.
No invented eval scores
Avoid fabricated BLEU/accuracy claims.
Refuse invented scale
Leave out employers, percentages, and capacity figures that are not in your Generative AI Engineer records.
Frequently Asked Questions
How do I prove ownership of production GenAI systems: fine-tuning, RAG, and multimodal applications on a Generative AI Engineer resume?
Cover production GenAI systems: fine-tuning, RAG, and multimodal applications with source-backed skills such as Python, PyTorch, Hugging Face Transformers, LoRA, QLoRA, plus experience or projects that show what you personally owned.
Which Generative AI Engineer skills belong in the skills section?
Prioritize Python, PyTorch, Hugging Face Transformers, LoRA, QLoRA and other category skills only when you can explain them with a project, production example, or troubleshooting story.
What should the Generative AI Engineer summary emphasize?
State about 4 years of Generative AI Engineer work and the Transformers/LoRA/vLLM-class generative AI delivery focus that matches the job description—only if that tenure is true for you.
Can indic-llm-bench support a thin experience section?
Yes—indic-llm-bench can support claims involving Python, PyTorch, Hugging Face Transformers, LoRA when you need concise, technology-specific project evidence.
Should I list credentials such as DeepLearning.AI Generative AI with LLMs, Hugging Face ML Course Certificate, on a Generative AI Engineer resume?
DeepLearning.AI Generative AI with LLMs, Hugging Face ML Course Certificate, or AWS Certified Machine Learning – Specialty belongs on the resume only when earned; otherwise rely on skills and delivery evidence.
Build Your GenAI Engineer Resume with AI
Showcase your fine-tuning wins, RAG architecture, and cost reduction metrics with an AI-crafted Generative AI Engineer resume.
Free to start · No credit card required