Hire a GenAI Engineer.
RAG, Fine-Tuning, Eval Pipelines.
DeFinitive's AI desk launches in 2026 with generative-AI engineering as a core specialism — engineers who take frontier models and turn them into shippable product features (RAG, fine-tuning, eval pipelines, LLM application code). Built on 200+ Web3 placements since 2021. Submit a brief and we'll come back with a written search plan.
Hiring a genai engineer well in 2026
GenAI engineering is the broad applied-AI track — the engineers who take frontier models and turn them into shippable product features. The role spans retrieval-augmented generation (RAG architectures, hybrid search, reranker design), fine-tuning (LoRA, full SFT, DPO, alignment fine-tunes), eval pipeline construction (offline + online + adversarial), and the operational discipline to keep a deployed LLM feature reliable as the underlying model moves underneath you.
It's the most common AI engineering role and also the most commonly mis-hired. Most generalist engineers can prototype with an LLM API; far fewer can ship one to production with the eval rigor to catch drift, the prompt-engineering discipline to survive a model swap, and the cost-engineering chops to keep unit economics workable at scale. The market price reflects the difference between the two profiles.
When DeFinitive runs a genAI engineer search, our sourcing strategy taps frontier-lab applied teams (the engineers shipping features inside Anthropic, OpenAI, Cohere), open-source contributors to LlamaIndex / Haystack / DSPy / LangChain, AI x crypto convergence engineers, and senior generalist engineers who pivoted post-2024 with shipped portfolios. The screen prioritises production deployments with measurable eval discipline, not prompt-engineering Twitter clout.
What this role typically owns
- ▸Design and ship RAG architectures — retrieval, ranking, context-window engineering, citation handling
- ▸Run fine-tuning workflows (LoRA, SFT, DPO) with proper eval gating
- ▸Build eval pipelines spanning offline benchmarks, online A/B, adversarial probing
- ▸Operate deployed LLM features through model swaps without regressions
- ▸Cost-engineer prompt + retrieval + inference paths to keep unit economics workable at scale
Signals we screen for
Every candidate passes a three-stage screen — technical, portfolio, culture. These are the proof signals that separate strong candidates from credentialed ones.
- ✓Production LLM features shipped to real users with measurable eval discipline
- ✓Survived at least one model swap (e.g. GPT-4 to GPT-4o, Claude 3 to 3.5) without product regression
- ✓Open-source contributions to LLM application frameworks (LlamaIndex, Haystack, DSPy)
- ✓Cost-engineering experience — prompt compression, prefix caching, retrieval pruning
- ✓Eval-first mindset — can articulate 3+ eval categories with examples from their own work
GenAI compensation in 2026
GenAI engineers in 2026 earn $150K (junior) to $240K+ (senior / staff) base salary, sourced from LinkedIn aggregate postings and frontier-lab compensation reporting. Total compensation including equity typically adds 30-60% at frontier labs and AI-native crypto firms. Senior tier compensation is rising 15-20% annually as production AI scales.
How the search runs
- 01
Brief (Day 0)
30-minute call with Nathan or the AI desk principal. Role spec, technical bar, compensation structure including equity / token grants.
- 02
Vetted shortlist (Day 3)
3-5 vetted candidates within 72 hours. Each passed our three-stage screen tuned for AI roles. Only 12% of sourced candidates make the shortlist.
- 03
Hire and pay (when they sign)
Pure contingency. You pay nothing until they accept and start. 60-day replacement guarantee.
GenAI Engineer hiring FAQ
How is a genAI engineer different from a machine-learning engineer?
A traditional ML engineer trains and deploys models — feature engineering, training pipelines, model serving infrastructure. A genAI engineer works with pre-trained frontier models — RAG, fine-tuning on top, eval pipelines, prompt engineering, application-layer integration. The skill sets overlap but the centre of gravity is different. Many strong genAI engineers came from ML-engineering backgrounds; many came from full-stack engineering. Our screen prioritises shipped genAI work over background credentials.
How will you assess production-readiness in genAI engineers?
Three signals matter most. First: have they shipped a feature to real users (not internal demos)? Second: can they articulate at least three eval categories with examples from their own work? Third: have they survived a model swap — moving a deployed feature from one frontier model to another without product regression? Candidates who clear all three are senior; candidates who clear two are mid-level; candidates who clear one are demo engineers.
How much do genAI engineers cost?
Junior $150K base, mid-level $195K median, senior / staff $240K+ at frontier labs and AI-native crypto firms reaching $280K-$320K base for the strongest candidates per public LinkedIn aggregate data. Total comp adds 30-60% via equity. Compensation has been rising roughly 15-20% annually since 2024 as production LLM scale grows.
How will you source genAI engineers?
Frontier-lab applied teams (Anthropic, OpenAI, Cohere, Mistral applied engineers), open-source contributors to LlamaIndex / Haystack / DSPy / LangChain, AI x crypto convergence engineers, and senior generalist engineers with shipped genAI portfolios post-2024. The DeFinitive AI desk is launching in 2026 — submit a brief and we will come back with a written sourcing plan.
How long will a genAI search take?
Our Web3 desk delivers a vetted shortlist in 72 hours and closes most roles in 4-6 weeks across 200+ placements since 2021. We are committing to the same operating standard on the AI desk. The genAI candidate pool is broader than safety or agent engineering, so candidate availability is rarely the bottleneck — calibration on production-experience requirements (RAG vs fine-tuning vs eval) is what usually drives the timeline.
Are genAI engineers usually remote?
Public posting data suggests roughly 75% of genAI roles are fully remote. The work is implementation-heavy and ships well across time zones. Frontier labs trend hybrid (SF, NYC, London); AI-native crypto firms are usually fully remote. Senior candidates concentrate in US west coast, NYC, London and Singapore.
Related
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