Hire AI Engineers

Ideaware places senior AI engineers, the people most job boards list as AI developers, on US product teams. They build features on top of large language models: retrieval (RAG), agents, document extraction, evaluation and the guardrails around them. You see vetted candidates within 48 hours, your pick starts in 8–12 days, and a senior AI engineer costs $6,000–10,000 a month all-in.

We screen for engineers who have put LLM features in front of real users. Anyone can call a chat API. The hard parts are retrieval quality, cost per request, evaluation, and what the feature does when the model is wrong.

What an AI engineer costs in the US

A US AI engineer earns an average base salary of $184,757 in 2026, with reported pay from $80,000 to $338,000, according to Built In. That sits well above the $135,980 median for all software developers (BLS, May 2025), and benefits add another 30% of employer compensation cost in US private industry (BLS, June 2026).

Teams that hire through us save 40–60% against a comparable US hire once benefits and fees count. The developer cost guide breaks down rates for every role.

The AI stack our engineers work in

Our AI engineers are software engineers first, with production Python or TypeScript behind them, who then specialized in LLM applications. Most profiles cover:

• Models and APIs: OpenAI, Anthropic Claude, Google Gemini, open-weight models such as Llama and Mistral; AWS Bedrock, Azure OpenAI and Vertex AI for hosted access
• Retrieval: embeddings, chunking strategies, hybrid search, pgvector, Pinecone, Weaviate or Qdrant
• Orchestration and agents: LangChain, LlamaIndex, tool calling, the Model Context Protocol, and plain code when a framework adds more than it saves
• Evaluation and observability: test sets for retrieval and answers, LLM-as-judge with human review, LangSmith or Langfuse traces, cost and latency tracking
• Application layer: Python with FastAPI, Node.js or Next.js for streaming UIs, Postgres, queues for long-running jobs
• Daily tools: Cursor, Claude, GitHub Copilot and ChatGPT for their own coding work

That last line matters as much as the others. In the 2025 Stack Overflow Developer Survey, 51% of professional developers used AI tools every day, and 46% said they distrust the accuracy of what those tools produce. We look for engineers who use the tools all day and check the output.

Vector databases and data pipelines

Retrieval quality depends on the store you pick and the pipeline that feeds it. Our AI engineers have run the common options in production and choose by your data, scale and hosting rules.

• pgvector when you already run Postgres and want embeddings next to the rest of your data, with one database to back up
• Qdrant for search with heavy metadata filtering, self-hosted or in its cloud, with payload indexes and quantization to keep memory in check
• Chroma for prototypes and local development, where you want retrieval running in minutes
• Pinecone or Weaviate when you want a managed service and don’t want to run the infrastructure
• Pipelines: Prefect or Airflow for scheduled ingestion, change detection so you only re-embed documents that changed, and a full re-embed plan for when you switch embedding models
• Search quality: hybrid keyword and vector search, rerankers, and recall measured against a labeled test set before and after each change

How we vet AI engineers

We accept about 3% of applicants. AI candidates go through our five steps: an async technical assessment, a 90-minute live coding session, an architecture review of an AI feature they shipped, an English and communication interview, and reference checks focused on production work.

The AI rounds cover:

• Retrieval design: how they would chunk, embed and rank a messy document set, and how they would measure whether retrieval works
• Failure handling: what the feature does on a wrong, empty or slow model response, and how they keep private data out of prompts and logs
• Evaluation: how they built a test set, what they measured, and one change they made because an eval failed
• Cost and latency: caching, model routing and batching, with numbers from a system they ran
• AI fluency: they work the live session with their own AI tools, and we watch how they prompt, review and correct the output
• Shipped work: a portfolio review limited to features real users touched, not notebooks or course projects

AI engineer, ML engineer, data engineer or NLP engineer?

Hire an AI engineer when you want to build product features on models that already exist. Pick a different profile when the work sits lower in the stack:

Not sure? Start with one AI engineer. They can tell you within a few weeks whether the product needs ML or data engineering help.

How to scope an AI engineer hire

Write down the outcome before you write the job post. “Cut first-response time on support tickets” gives a candidate something to design against; “add AI” doesn’t. Then name the specialization (LLM features, classic ML, computer vision) and the stack the engineer has to fit into.

In interviews, give finalists a small task on a sanitized sample of your real data and ask them to walk you through their approach. You learn how they think about accuracy, edge cases and cost, which a résumé won’t show. If you want developers who use AI tools well across your whole codebase, not AI specialists, read our guide to AI-native developers.

Frequently asked questions

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