Axial Search recruits for AI transformation roles across North America, building long-term relationships with leaders across every industry vertical. Apply today to express your interest in this position and others like it. Visit our website to learn more about our process and to find free tools to support your job search — including our live job market dashboard with salary, skills, and hiring-trend data from thousands of AI transformation roles.

What the market looks like

AI engineering has emerged as a distinct track from traditional ML engineering, driven almost entirely by the rise of generative AI in product. We track over 10,000 US postings that explicitly call for LLM and generative AI experience, and the hiring is densest across Bay Area product companies, New York fintech and consumer technology, and a growing remote contingent. Mid-senior base compensation sits in a $130K–$230K band, and the strongest AI engineers we place combine shipping instincts with a sharp evaluation mindset.

Job responsibilities

Build LLM-powered product features using prompt engineering, retrieval-augmented generation, and orchestration frameworks
Integrate foundation model APIs (OpenAI, Anthropic, Google, and open-weight alternatives) into production surfaces
Design and maintain evaluation frameworks that catch regressions, track cost, and measure real-world model quality
Collaborate with product, design, and backend teams to ship AI features end-to-end, not just deliver the model layer
Stay close to the frontier of generative AI tooling and translate new capabilities into product opportunities
Contribute to agent, tool-use, and workflow orchestration architectures as they mature
Write production-grade Python and collaborate fluently across ML and software engineering teams
Help set engineering practices for the still-forming discipline of AI engineering, from testing patterns to observability

Candidate requirements

3+ years of software engineering experience, with at least 1–2 years building on LLMs or generative AI
Strong Python and experience with LangChain, LlamaIndex, or equivalent orchestration frameworks
Practical experience with RAG — embeddings, vector databases (Pinecone, pgvector, Weaviate), and retrieval quality
Comfort with cloud infrastructure (AWS, Azure, or GCP) and containerized deployments
Track record of shipping user-facing AI features in production
Strong evaluation mindset — offline and online, including human-in-the-loop review

AI Engineer