Billy Bonaros

Data Scientist @ Persado | Co-founder of the Data Science blog:

[Andrew Ng @AndrewYNg]

One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tuning agentic workflows that suit the client’s particular needs. I’ve heard from people who are wondering anew about the FDE career path since OpenAI and Anthropic started building new teams to place FDEs within client organizations.

The rise of FDEs for AI workloads is one way AI is creating new jobs (and why the jobpolcalypse narrative of upcoming job market collapse is false -- there will be many AI and non-AI jobs). However, I believe there will be far more AI Engineer jobs than FDEs, as I explain below.

The FDE role was pioneered about two decades ago by Palantir, which sent engineers to government locations to work on secure, air-gapped networks. In addition to having good technical skills, FDEs need communication skills and sometimes business skills. For example, they may need to speak with clients to understand their needs, formulate a strategy to prioritize projects, explain complex technology, and respectfully push back if a client asks for something unrealistic. They’re enjoying a resurgence because of the amount of work involved in taking an off-the-shelf LLM and building it into a custom agentic workflow that fits particular business needs.

However, I believe the number of AI Engineer jobs will be far larger. A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects. While my organizations do hire FDEs, we hire far more AI Engineers! Also, a common client concern is that it is hard to find vendor-neutral FDEs — they are, after all, there to deeply integrate a particular vendor’s product into a company. In this moment when it’s hard to predict which AI service will be the best one in a year’s time, optionality (the ability to pick whatever vendor turns out to fit best in the future) is very valuable. In contrast, letting FDEs tightly bind a company’s processes significantly reduces optionality.

Right now, I see surging demand for AI Engineers who can build software applications using AI software components (like LLM prompting, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode). As the AI Engineer role matures, I expect it to fragment into more specialized roles, like the generic Software Engineer role from decades ago fragmented into frontend, backend, mobile, data engineering, devops, and so on.

What will be the future, specialized AI engineering roles? I don’t know. Perhaps there will be AI FDEs, LLMOps Engineers, Evals Engineers, AI Data Engineers, Harness Engineers, and other roles we don’t have names for yet. But for now, I see a lot of AI engineers who are generalists create a lot of value. Skilled AI Engineers are in very high demand! As our field continues to mature over the coming decade, I look forward to new specializations within AI Engineering that create even more job opportunities.

[Original text: The Batch newsletter]

[Predictive Hacks @predictivehacks]

Time Series Decomposition In Python https://t.co/Yf2ltJR7ag via @YouTube

#Python #DataScience #RStats #tutorial #TimeSeries

[Python Hub @PythonHub]

Fast & Easy Python APIs Using FastAPI

One of the best python frameworks!Continue reading on Towards Data Science ...

https://t.co/wy9dxEpmX4

[Towards AI @towards_AI]

How To Create An Instagram Profile Analyzer App Using Python And Streamlit by Billy Bonaros via #TowardsAI → https://t.co/KFDDOexQla #MachineLearning #ML #ArtificialIntelligence #AI #DataScience #DeepLearning #Technology #Programming #news #research #Science

[Predictive Hacks @predictivehacks]

Prevent Overfitting

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[Towards Data Science @TDataScience]

How To Get All Orders From Shopify by @BonarosBilly https://t.co/gZOxJJQeJH

[Predictive Hacks @predictivehacks]

How to find the probability of common birthday using Monte Carlo Simulation
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#rstats #probability #statistics #DataScience #DataAnalytics #DataVisualization

[Towards Data Science @TDataScience]

Time series decomposition is a technique that splits a time series into several components. @BonarosBilly explains how to automatically decompose a time series with Python. https://t.co/U5i3rvZrON

[Billy Bonaros @BonarosBilly]

@Roger_M_Taylor Thanks!

[Predictive Hacks @predictivehacks]

Anna Karenina Principle In Data Science
#DataScience #rstats #Python #pythonlearning
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[Billy Bonaros @BonarosBilly]

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#python #DataScience #time_series

[Towards Data Science @TDataScience]

Python Rest API Example by @BonarosBilly
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Python Rest API Example https://t.co/QfQ938yybF #GetMoreInsights #CoolerData #Data #DeepLearning #DataScience #ArtificialIntelligence #Analysis #Innovation

[Billy Bonaros @BonarosBilly]

@CoolerData thanks for sharing!

[machine learning @Mlearning_ai]

How to get a Data Science Job without Experience by @predictivehacks https://t.co/9ENwrR2Hu6
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[Towards AI @towards_AI]

Rule-Based Matching for NLP using spaCy by Billy Bonaros via #TowardsAI → https://t.co/aRfGdFeoOF #MachineLearning #ML #ArtificialIntelligence #AI #DataScience #DeepLearning #Technology #Programming #news #research #Science

[Towards AI @towards_AI]

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[Billy Bonaros @BonarosBilly]

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[Towards AI @towards_AI]

Practical Guide: Build and Deploy a Machine Learning Web App by Billy Bonaros via #TowardsAI → https://t.co/ZSgUyhTNom #MachineLearning #ML #ArtificialIntelligence #AI #DataScience #DeepLearning #Technology #Programming #news #research #Science

[Billy Bonaros @BonarosBilly]

“Practical Guide: Build and Deploy a Machine Learning Web App” by Billy Bonaros
https://t.co/ut9kPdmXpT