Simor Consulting
Category: Thought Leadership
There is a version of the data engineering career that nobody warns you about. It is not the startup grind or the big-company bureaucracy. It is being the only data engineer on a team of people who do
In 2023, I rewrote a data pipeline three times because the framework landscape kept shifting. First it was built on LangChain. Then the team wanted to switch to LlamaIndex because it handled retrieval
A critical open-source library used by thousands of companies, including several Fortune 500 firms, is maintained by one person in their spare time. This is not a hypothetical. It is a description of
LinkedIn's latest workforce report shows "AI engineer" as the fastest-growing job title for the third consecutive quarter. Job postings containing the title increased 280% year-over-year. The growth r
A Fortune 500 company hired a team of twelve machine learning engineers and tasked them with building a predictive maintenance system for their manufacturing floor. The ML team spent four months evalu
Most reading lists for AI leaders are assembled by people who sell AI. The lists are full of books about machine learning techniques, deep learning architectures, and the latest framework documentatio
Three years ago, "data engineer" was a coherent job title. You built pipelines, managed infrastructure, and moved data from where it was to where it needed to be. The role required SQL, Python, and a
A data engineering team I worked with had adopted six AI-powered tools in twelve months. An automated code reviewer, a data quality scanner, a pipeline orchestrator with intelligent retry, a natural l
OpenAI shipped GPT-5. Anthropic shipped Claude 4. Google shipped Gemini Ultra 2. Within six weeks of each other, the three leading model providers released frontier models that are, by most benchmarks
A hiring manager at a large tech company told me they had four hundred engineers working on their AI platform and zero people with training in philosophy, ethics, or the social sciences. When I asked
The legal system has not caught up with the practice of training AI models on copyrighted data, and the people building AI systems are not waiting for it. Models trained on books, articles, code repos
The phrase "data-driven culture" has been emptied of meaning by overuse. It appears in every strategy deck, every job posting, every conference talk. Everyone claims to want it. Almost no one can desc
In 2009, deploying software to production was an event. It involved a change request, a maintenance window, a runbook, and a prayer. Developers wrote code, then threw it over the wall to operations, w
I was in a strategy session where a VP of Data told the room that generative AI would "eliminate the need for data analysts within two years." The room nodded. Budget was reallocated. Three analyst po
The CTO of a mid-size financial services firm told me they had spent $4 million on AI tooling in eighteen months. They had three large language model providers under contract, a vector database cluste
A technology company built an impressive AI platform. They had GPU clusters, fine-tuning pipelines, evaluation frameworks, and a growing model registry. They opened access to any team that wanted to u
A financial services firm spent eight months building an AI-powered document analysis system. When it came time to deploy, they discovered their retrieval system had no governance layer, their agent h
2025 was the year AI moved from experimentation to industrialization. While 2024 saw the explosion of generative AI capabilities, 2025 was about making those capabilities production-ready, cost-effect