We’ve never done an episode like today’s… instead of covering a specific data science-related topic, in today’s episode I’m letting you know about a critical role that we’re hiring for on the SuperDataScience Podcast. Perhaps you are the person we’re looking for or you know the person we are looking for!
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Filtering by Tag: aiengineer
The Fastest-Growing Jobs Are AI Jobs
Assessing the fastest-growing job is tricky. For example, using job-posting data isn’t great because there could be lots of duplicate postings out there or a lot of the postings could be going unfilled. Another big issue is defining exactly what a job is: The exact same responsibilities could be associated with the job title “data scientist”, “data engineer” or “ML engineer”, depending on the particular job titles a particular company decides to go with. So, whoever’s evaluating job growth is going to end up bucketing groups of related jobs and responsibilities into one particular, standardized job-title bucket, probably these days in a largely automated, data-driven way; if you dug into individual examples, I’m sure you’d find lots of job-title standardizations you disagreed with but some kind of standardization approach is essential to ensuring identical roles with slightly different job titles get counted as the same thing.
Read MoreAI Engineering 101, with Ed Donner
My holiday gift to you is my Nebula.io co-founder Ed Donner, one of the most brilliant, articulate people I know. In today's episode, Ed introduces the exciting, in-demand "A.I. Engineer" career — what's involved and how to become one.
After working daily alongside this world-class mind and exceptional communicator for nearly a decade, it is at long last my great pleasure to have the extraordinary Ed as my podcast guest. Ed:
• Is co-founder and CTO of Nebula, a platform that leverages generative and encoding A.I. models to source, understand, engage and manage talent.
• Previously, was co-founder and CEO of an A.I. startup called untapt that was acquired in 2020.
• Prior to becoming a tech entrepreneur, Ed had a 15-year stint leading technology teams on Wall Street, at the end of which he was a Managing Director at JPMorganChase, leading a team of 300 software engineers.
• He holds a Master’s in Physics from the University of Oxford.
Today’s episode will appeal most to hands-on practitioners, particularly those interested in becoming an A.I. Engineer or leveling up their command of A.I. Engineering skills.
In today’s episode, Ed details:
• What an A.I. Engineer (also known as an LLM Engineer) is.
• How the data indicate A.I. Engineers are in as much demand today as Data Scientists.
• What an A.I. Engineer actually does, day to day.
• How A.I. Engineers decide which LLMs to work with for a given task, including considerations like open- vs closed-source, what model size to select and what leaderboards to follow.
• Tools for efficiently training and deploying LLMs.
• LLM-related techniques including RAG and Agentic A.I.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
MLOps: The Job and The Key Tools, with Demetrios Brinkmann
Today, global MLOps community leader Demetrios Brinkmann details why MLOps is essential, how it differs from related roles like LLMOps, DevOps and A.I. Engineering, and the best tools for deploying and scaling LLMs.
Demetrios:
• Is Founder and CEO of MLOps Community, an organization dedicated to supporting MLOps professionals that has quickly grown to over 20,000 members.
• Was previously founder of the Data on Kubernetes community.
• Before that, worked in public-facing roles at a number of European tech startups.
Today’s episode will be of interest to anyone who’s keen to better understand the critical function of MLOps in bringing machine learning models to the real world.
In today’s episode, Demetrios details:
• What exactly MLOps is and how it relates to other jobs like LLMOps, DevOps and A.I. Engineer.
• The key MLOps tools and approaches.
• What it takes to build a thriving community of tens of thousands of professionals in just a few years.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.