Your traditional ML hiring process won't work for generative AI specialists. The skills are different, the evaluation looks different, and the market is moving at light speed.
What Is a Generative AI Engineer?
Generative AI engineers are a different specialization from traditional machine learning engineer hiring — the skills, evaluation approach, and market all look different. Traditional ML engineers build custom models from domain data, with slower iteration cycles (retraining) and quantitative evaluation metrics. Generative AI engineers work with pre-trained foundation models, adapt and apply them, iterate faster through prompt changes and fine-tuning, and deal with both quantitative and qualitative evaluation. They work with real-time serving and API integration rather than batch serving.
Sub-specializations include: LLM Application Engineers who build with existing APIs (OpenAI, Anthropic, Meta), handle prompt engineering, and integrate with downstream systems; LLM Fine-Tuning specialists who adapt open-source models to specific domains; LLM Infrastructure Engineers who serve models at scale, optimize latency, and handle quantization; Prompt Engineering & Evaluation specialists; and AI Product Engineers who build products with generative AI.
Core Skills to Look For
1. Deep Understanding of Transformers & LLMs: How transformers work (attention, self-attention, multi-head attention), different LLM architectures, context windows and their implications, differences between models (GPT, Claude, Llama, Mistral). Red flag: "Transformers are just neural networks." Green flag: can explain the attention mechanism and why it enables long-range dependencies.
2. Prompt Engineering Mastery: Understanding of different techniques (zero-shot, few-shot, chain-of-thought, role-playing), ability to diagnose why a prompt isn't working, systematic approach to improving prompts, understanding of temperature, top-k, top-p. Red flag: "I just write instructions and hope for the best." Green flag: has a framework for iterating and measuring quality.
3. RAG Implementation: Retrieval + generation architecture, chunking strategies, embedding models and vector databases, retrieval quality evaluation, end-to-end pipeline building. Awareness that RAG mitigates (not eliminates) hallucinations by grounding in retrieved documents.
4. Fine-Tuning Knowledge: When to fine-tune vs. use base model, approaches (full fine-tuning, LoRA, adapters, prompt tuning), data preparation, evaluation after fine-tuning, cost-benefit analysis. Strong candidates have opinions about LoRA vs. full fine-tuning based on actual trade-offs.
5. Evaluation of Generative Outputs: Automatic metrics (BLEU, ROUGE, BERTScore), LLM-as-judge, human evaluation rubrics, A/B testing in production. Red flag: "We'll just look at the outputs manually." Green flag: proposes a systematic evaluation approach.
Interview Questions That Reveal Real Expertise
Explain How Transformers Work: Look for understanding of the attention mechanism, why it works, limitations (O(n²) complexity with context length). Strong answer: "Transformers use attention to let each token attend to all other tokens. Self-attention computes query, key, value vectors for each token. Multi-head attention lets the model attend to different representation subspaces. Main limitation is O(n²) complexity."
Why RAG Instead of Fine-Tuning? Look for trade-off understanding: RAG is faster and cheaper for knowledge that changes; fine-tuning is better for style adaptation or stable knowledge. Strong answer discusses when each makes sense and why.
Your LLM Is Hallucinating. Walk Me Through Debugging: Look for a systematic approach — define what hallucination means in context, check the prompt and retrieved documents, check the model, propose mitigation strategies. Acknowledge it's not fully solvable.
Explain Chain-of-Thought Prompting: Making reasoning explicit helps the model. Works well for math, logic, multi-step problems. Doesn't help for tasks without clear reasoning. Increases cost and latency.
Design an LLM-Powered Customer Support System: They should cover question routing (intent classification), escalation to humans, knowledge sources, evaluation metrics (customer satisfaction, resolution rate), cost considerations (API calls), hallucination mitigation, system monitoring.
Red Flags, Green Flags, and Assessment Process
Green flags: Can explain transformers and attention clearly. Has hands-on experience with LLM APIs or open-source models. Thinks systematically about evaluation. Acknowledges limitations of LLMs ("hallucinations are hard to solve"). Asks clarifying questions about your use case. Shows awareness of costs and trade-offs. Can discuss multiple models and approaches.
Red flags: Says "just use GPT, it solves everything." Can't explain how attention works. No experience shipping anything with LLMs. Dismisses evaluation as "just looks good to me." Overpromises on accuracy/reliability. No awareness of costs. Only knows one framework/model.
Assessment Process: Stage 1 (30 minutes) — quick technical screen. Many teams automate this with an AI interviewer so no engineer time is required at this stage. on transformer basics, fine-tuning vs. prompt engineering, RAG architecture, hallucination debugging. Pass/fail: can they articulate understanding of fundamentals?
Stage 2 (90 minutes) — Part A: design problem (30 min) presenting an LLM application scenario; Part B: experience deep dive (30 min) on real projects they've built; Part C: hands-on (30 min) implementing a core piece.
Stage 3: Take-home project (4–6 hours). Good options: build a RAG system for a dataset, fine-tune a small model, create a prompt optimization framework, build an evaluation system. Evaluate on approach, completeness, code quality, communication of choices.
Compensation, Sourcing, and Onboarding
Generative AI specialists command a premium over traditional ML engineers due to high demand and scarce supply. US-based ranges (adjust for your market): Entry-level (0–2 years LLM experience) $150–220K + equity; Mid-level (2–5 years) $220–320K + equity; Senior (5+ years, proven expertise) $320–450K+ + equity.
Where to find generative AI engineers: Look for people active on Hugging Face Hub, contributing to LLM open-source projects (vLLM, LLaMA, etc.), writing or speaking about LLMs publicly. Sourcing channels include generative AI communities (Discord servers, Reddit r/MachineLearning), Hugging Face job board, AI-focused job boards, and direct outreach to active contributors.
Onboarding: Week 1 — understand your LLM use cases, have them build a simple prompt and RAG system hands-on, explore existing LLM infrastructure. Weeks 2–3 — deep dive into your domain, improve an existing prompt or system, start contributing. Month 2 — scope and implement a real project with mentorship. Month 3 — review learnings, identify strengths and growth areas, plan next quarter.
Start recruiting early. Machine learning engineer hiring at this specialization level requires pipeline building months in advance. The best candidates are hard to find because demand is so high. Build your pipeline before you need it.
Frequently asked questions
What skills should a generative AI engineer have?
Core skills include Python proficiency, experience with LLMs (GPT-4, Claude, Llama), RAG system design, prompt engineering, fine-tuning workflows, and evaluation of LLM outputs. Strong generative AI engineers also understand the trade-offs between retrieval-based and fine-tuning approaches, and have experience deploying LLM-powered applications in production.
How do you assess generative AI engineers in an interview?
Use scenario-based technical questions: ask them to design a RAG system for a specific use case, explain when they would fine-tune versus use retrieval, and describe how they evaluate LLM output quality in production. A practical assessment involving a real LLM task is the most reliable signal.
What is the difference between a generative AI engineer and a machine learning engineer?
A machine learning engineer typically works on traditional ML systems — classification, regression, recommendation, forecasting. A generative AI engineer specializes in large language models, foundation models, and AI-generated content — LLM orchestration, prompt design, and RAG architecture.
How much do generative AI engineers earn in 2026?
Generative AI engineers command a 10–20% premium over standard ML engineer salaries per the Robert Half 2026 Salary Guide. The national median ML engineer base salary is $161,030 (Glassdoor, Feb 2026), with senior total compensation regularly exceeding $350,000 when equity is included.
What is the best coding assessment for generative AI roles?
The best assessment for generative AI candidates uses realistic LLM tasks — not algorithm problems. Include prompt engineering exercises, RAG pipeline design, LLM output evaluation, and practical implementation. Codeaid offers domain-specific assessments covering Generative AI and LLMs, evaluated automatically with detailed scoring reports.
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