AI Engineer vs LLM Engineer:
Same Role, Different Names?

In practice, minimal to none. Both titles typically describe engineers who build applications using large language models, work with AI APIs, implement RAG systems, and deploy AI-powered features. The 'AI Engineer' title is more established and broader. 'LLM Engineer' is newer and explicitly references the technology, but the job is usually the same. Some companies use 'LLM Engineer' for roles focused on model fine-tuning or optimization, but this is inconsistent.

Usually, yes. Most job postings use these titles interchangeably. Both involve: working with LLM APIs (OpenAI, Anthropic, Google), building RAG systems, implementing AI agents, deploying to production, and monitoring AI applications. If you're qualified for one, you're typically qualified for the other. The title choice often reflects the company's branding or the hiring manager's preference, not a meaningful role distinction.

In a minority of companies: 'LLM Engineer' may focus specifically on model-level work—fine-tuning, optimization, evaluation, or working with open-source models. 'AI Engineer' may be broader, including computer vision, audio, or other AI domains beyond language models. If the job description mentions model training, RLHF, or deep optimization work, that's a signal the LLM Engineer role differs from typical AI Engineering. Otherwise, assume they're the same.

Use 'AI Engineer' for broader appeal—it's the more established title and applies regardless of which specific AI technologies you work with. If you're targeting roles specifically focused on language models at LLM-focused companies, 'LLM Engineer' signals domain specialization. Many engineers list both: 'AI/LLM Engineer' to match both search terms. Let the job posting guide your resume customization.

Absolutely. Apply to both since they usually describe the same role. Read each job description to confirm the requirements match your skills, but don't filter by title alone. Some companies posting 'LLM Engineer' roles will interview candidates with 'AI Engineer' experience and vice versa. The skills transfer completely between these title variations.

No meaningful difference. Both roles typically range from $130K-$250K depending on experience, location, and company. Since they're usually the same job with different names, compensation is driven by your experience and negotiation skills, not which title appears on the offer letter. Focus on building skills and demonstrating impact, not chasing one title over another.

Both roles typically require: Python proficiency, experience with LLM APIs (OpenAI, Anthropic), understanding of RAG systems and vector databases, production deployment experience, and software engineering fundamentals. Prior ML experience helps but isn't always required. The emphasis is on building working applications, not research or model training.

3-6 months with an existing software engineering background. You're learning to work with LLM APIs, build RAG systems, and deploy AI applications. If you're new to programming, add time for Python and software engineering fundamentals. The path is the same regardless of which title you're targeting.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.