Artificial intelligence and machine learning hiring guides, AI engineer salary benchmarks, ML team structure advice, and industry trends shaping AI talent acquisition in 2026 and beyond.
By Colin Boothe, CIO at Foretopia AI data cleaning uses machine learning and language models to find duplicate records, standardize formats, and flag missing or wrong values, then proposes fixes for a person to approve. It matches fast and decides badly. So write the rules first, and clean only what your next AI project reads. … Read more
By Tom Kenaley, President and Senior Partner, KORE1 A generative AI engineer job description covers an engineer who builds product features on foundation models like GPT, Claude, and Gemini, owning retrieval, evaluation, guardrails, and inference cost, usually without training a model from scratch. Most postings under that title in 2026 describe somebody else. The template … Read more
By Colin Boothe, CIO at Foretopia AI in accounting speeds up the close by handling pattern work: matching invoices, flagging outliers, drafting a first-pass flux narrative. It does not make judgment calls like materiality or revenue recognition. Those still need a controller, and most vendor pitches blur that line on purpose. A controller I worked … Read more
By Colin Boothe, CIO at Foretopia AI governance for a mid-market company is a short written standard that says which tools people may use, on which data, and who checks the output before it reaches anyone outside the room. It fits on one page. Companies get this wrong by building a committee first and writing … Read more
By Mike Carter, Director of Partnership Success, KORE1 A computer vision engineer job description needs four facts up front: the sensor, the deployment target, the latency budget, and whether the job is training models or shipping them. Without them, a research scientist, an autonomous-vehicle perception engineer, and an industrial machine vision specialist all read the … Read more
By Mike Carter, Director of Partnership Success, KORE1 An AI/ML org chart works when the ratios do: roughly one platform engineer per three modelers, one data engineer per two, and no more than eight people reporting into any single lead. Argue about the rest of it all you want. Those three numbers hold, and the … Read more
By Kris Drouet, Engineering Executive, in partnership with KORE1 AI implementation cost after a successful POC usually lands at three to five times the pilot price, because inference at real volume, data work, evaluation, and the people who run it are never inside the demo number. The pilot priced a rehearsal. Production prices the run. … Read more
By Colin Boothe, CIO at Foretopia AI in ERP is real today in three narrow places, which are document capture, drafted text, and natural-language reporting. Everything wearing the word autonomous is a roadmap slide with a delivery window on it. The sorting rule is whether you can switch a thing on this afternoon without starting … Read more
By Colin Boothe, CIO at Foretopia Build your first agentic AI workflow by picking one boring, high-volume task, writing its rules down like onboarding notes, giving it two or three narrow tools, and making it stop for a human wherever mistakes are expensive. Start with a workflow, not an agent. The stopping rules are the … Read more
By Mike Carter, Director of Partnership Success, KORE1 Hiring an AI agent engineer in 2026 costs roughly $150,000 at the median and north of $270,000 at the 75th percentile, and the deciding skill is production reliability, not model knowledge. The demo is never the hard part. Keeping the thing correct on the four hundredth run … Read more
