AI Engineer

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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