Vincent Granville

BondingAI

GenAI platform based on xLLM, the game-changing technology that started the LLM 2.0 revolution. Boosting productivity, automating tasks, delivering ROI at scale to Enterprise customers. Without GPU, hallucinations, security issues, or latency. With zero weight, real-time fine tuning, exhaustive but concise results, customizable relevancy scores, and a modern UI - not just a prompt box.

Chief AI Architect, Co-Founder & Investor

NVIDIA

Advocate for products and services pertaining to AI, generative AI and LLMs. Featuring curated Nvidia content in my GenAI newsletter (200k subscribers) and other media, presenting at selected Nvidia events. Deep understanding of the AI landscape and technology, building ground-breaking architecture known as LLM 2.0 / xLLM, with our startup BondingAI.io participating in the Nvidia Inception program.

AI Advocate & Startup Inception Program

GenAItechLab.com

Now part of BondingAI.io. Building better, faster, and less expensive GenAI products with explainable AI, such as NoGAN (data synthesizer). Key product:  xLLM, a fast in-memory agentic multi-LLM system built from scratch, with zero weight, no training, no latency, no hallucination, fully contextual from the ground up, with real-time fine-tuning. For scientific or enterprise search, as well as clustering and predictions.  Also, builder of the best model evaluation metric on the market.

Founder, Lead AI Architect

AB InBev

Helping Fortune 100 company InBev implement my xLLM technology as an alternative to OpenAI and LangChain to boost relevancy (hallucination-free), reduce latency, and increase speed by several orders of magnitude, while increasing security via sub-LLMs accessible to authorized users only. Offering a better solution for search and cataloging, to help users navigate their corporate repository. With a focus on concise but exhaustive results (via augmentation) and specialized sub-LLMs. Local implementation without external API calls.

GenAI & LLM Consultant

MLtechniques.com

AI optimization research lab, developing state-of-the-art technology with a focus on high quality and fast algorithms, thus cost effective. Our GenAI Web APIs are accessible on GenAItechLab.com. Covering synthetic tabular data, artificial DNA sequences, time series & geospatial data generation, agent-based modeling, synthetic graphs, and customized LLMs.

Chief AI Scientist

Data Science Central

The company was acquired in 2020.  I am still a contributing author.

Executive Data Scientist, Co-Founder, Managing Partner

Microsoft

Developed a strategy to collect the right data and detect minuscule change points in time series associated with KPI's (when the changes occur, their amplitudes and directions). Goal: assessing the impact of events influencing core metrics. Example: changing the definition of a website user resulting in shift in user count, or changing some features on Bing.com resulting in increase / decrease in pageviews. These were blind tests: I did not know when the website changes were made. But when I detected a change, it was matched against known events that took place at the time flagged by my algorithm as "change point". I designed and then helped automate this algorithm.

Machine Learning Consultant

LookSmart

Optimizing advertising campaigns, Ad arbitrage. Projects and technology include:

• Created list of top commercial keywords accounting for 85% of all pay-per-click revenue
• Leveraged Google API's to automatically find millions of high yield keywords 
• Automated taxonomy creation (NLP)  
• Click fraud detection (click jacking and Botnet detection)
• Internet traffic quality scoring using hidden decision trees 
• Automation of keyword bidding 
• Reverse engineering Google keyword pricing algorithms, for ad arbitrage

Chief Scientist

eBay

Keyword pricing optimization. Improving keyword bid pricing algorithm to update bids on millions of keywords purchased on Google AdWords. Automated daily updates. Setting bids for large tail of keywords with no conversion history, based on aggregation methods (NLP). Detection of new keywords.

Data Science Consultant

Adometry (acquired by Google)

Built scoring system to measure traffic quality associated to keywords and publishers, for keyword ad networks, search engines, or advertisers (the customers). Scores were used for fraud detection, removal of bad publishers and keyword pricing. Detected major Botnets. Developed proofs of concept, attracting Looksmart and eBay as customers.

Chief AI Scientist, GenAItechLab.com

Full-stack entrepreneur with multimillion-dollar exit. Co-Founder at BondingAI.io. Author (Wiley, Elsevier). Creator of open-source Python libraries / Web APIs such as xLLM and GenAI-Evaluation. Builder of high performance in-memory agentic multi-LLMs for professional users and enterprise, with real-time finetuning, self-tuning, no weight, no training, no latency, no hallucinations, no GPU. Made from scratch, leading to replicable results, leveraging explainable AI, adopted by Fortune 100. With a focus on delivering concise, exhaustive, relevant, and in-depth search results, references, and links. 

Vincent is a leading expert, mathematician, patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay.  Vincent also founded and co-founded a few startups, including one with a successful exit (Data Science Central acquired by Tech Target). He is also a top influencer for brands such as Nvidia, SingleStore, and The Linux Foundation.

Privately funded GenAI and ML Optimization research lab, creating and disseminating open source intellectual property. Developing explainable and generative AI, synthetic data, computer vision, NLP and generic ML technologies. Check out our certifications, Python libraries, eBooks, articles, courses and podcasts, including our best seller "Synthetic Data and Generative  AI" (Elsevier). Visit our company website at MLtechniques.com.

If you were to work with us, you need to un-learn everything you know about LLM/RAG. We have a radically different approach to build them, and we prefer someone who knows nothing on the topic, to someone who knows what everyone else does. Just to give an example, see how we evaluate them, very different from what you learned in school or in your previous job:  https://lnkd.in/g8g7_JEe

Chief AI Architect & Co-Founder, BondingAI.io

Learn AI Together - I share my learning journey into AI & Data Science here, 90% buzzword-free. Follow me and let's grow together!

"Which AI role suits you?"

An interesting matrix, and I like the idea.

It might simplify reality a bit, but it’s directionally accurate and still useful as a reference to plan your learning path and get a quick sense of:
- What AI roles exist
- What core skills each role requires

If you're aiming for a specific role, this helps you understand what’s critical vs “nice to have.”

Also here are a few thoughts I’d add, especially from a GenAI lens (understandably hard to cover all in one graph):

▪️In the matrix, LLMs, RAG, and Agents are grouped as one row -
but in practice, these are completely different skill sets (imo):

→ Prompting ≠ Retrieval-Augmented Generation ≠ Multi-Agent Orchestration
(And most teams today need all three.)

▪️Worth noting a few other key skills that often come into play in production GenAI systems:
→ Evaluation and observability
→ Guardrails, tool-calling, tracing
→ Feedback loops, context management, orchestration logic

▪️Business roles are much more dimensional in practice -
For example, AI PMs and Ethics leads often need deep understanding of compliance, risk, model lifecycle… not just “soft skills.”

Still, I like it.
It’s a great starter, and a helpful way to assess your current strengths or identify team gaps.

📍If you're building agents, copilots, or just trying to level up in the AI space, we’ve started a free AI community where people learn, share, and build together:
🔗 https://lnkd.in/gAE32Fae

Workshops, challenges, courses, and a lot of smart folks figuring it out together.

And if you'd like to join us, explore our public investment:
🔗 https://bit.ly/41JeDNO

Image: SuperDataScience

Curious:
Does this reflect your current AI role?
Any skills you’d add or move?
__________

For more on AI and learning materials, plz check my previous posts. 

I share my journey here. Join me and let's grow together. Alex Wang

#agenticai #aienginnering #technology #artificialintelligence

How about optimizing AI so that it delivers ROI? I will be discussing this topic at https://lnkd.in/gNsPj6AA

No BS AI/ML Content | ML Engineer with a Plot Twist 🥷30M+ Views 📝

Paul Iusztin just dropped the best context engineering guide I've seen.

And it's free.

While everyone's arguing about prompt templates, 
he's showing why we're all doing AI wrong.

The brutal truth:

Context engineering isn't "better prompts."
It's architecting your AI's entire information ecosystem.

What Paul revealed:

✅ LLMs are the CPU. Context window is the RAM.
✅ You need 5 memory types (not just chat history)
✅ Context decay starts at 32K tokens (not 2M)
✅ YAML beats JSON by 66% in token efficiency

The shift nobody's talking about:

2022: Craft perfect prompts
2023: Add RAG for context  
2025: Become the RAM manager for your AI

My favorite insight:

"Context engineering is finding the ideal set of functions to assemble a context that maximizes output quality."

Paul covers:
→ The 5 components of context
→ Production challenges that kill 90% of projects
→ Optimization strategies that actually work
→ Real code examples (not pseudocode)

Translation: Stop dumping. Start designing.

This isn't another "10 prompt tricks" article.
It's the engineering discipline that separates production AI from demos.

Check it out on Decoding ML.

♻️ Repost to help someone graduate from prompt engineering to actual AI architecture

That won't cut it if you interview with us. Our LLM is radically different, most of these concepts apply to old-fashioned architecture. We don't generate hallucinations, we don't need prompt engineering, benchmarking metrics fail to capture the most important qualities. We don't even use embeddings and cosine similarity. If you bring this stuff in an interview with us, you will look to us like a dinosaur. If you know nothing about LLMs, at least you do not start the interview with a negative connotation. 

Just to mention one point, see my article on evaluation metrics: https://lnkd.in/g8g7_JEe

View Dr Chiranjiv Roy, PhD, MBA’s profile

Your GenAI interview is won (or lost) in the first 15 minutes. Questions and tips!!

Here are 22 real questions you must be ready to answer:

LLM Fundamentals
	1.	What does tokenization really mean, beyond just “splitting text”?
	2.	Why are decoder-only models dominant today? When would encoder-decoder be a smarter choice?
	3.	Explain attention in plain English — where exactly do queries, keys, and values come into play?
	4.	Compare sampling methods: top-K, top-P, and temperature. When would you choose each one?

Prompting & Context Engineering
	5.	Share an example where a zero-shot prompt failed. How did you fix it?
	6.	How do you track versions of prompts and test them for consistency?
	7.	What is “context window waste” and how would you reduce it?

Fine-Tuning & Alignment
	8.	LoRA vs QLoRA vs PEFT — how do you balance memory, speed, and accuracy?
	9.	What are the biggest limitations of RLHF? Give one real failure case.
	10.	When would you pick an open-source model over a proprietary one for fine-tuning?

RAG (Retrieval-Augmented Generation)
	11.	Walk me through how embeddings and similarity search actually work.
	12.	Pinecone vs Chroma vs Weaviate — which one would you choose and why?
	13.	When does hybrid retrieval (vector + keyword) outperform pure vector search?

Agentic AI
	14.	Define an AI agent without using buzzwords.
	15.	How do you handle tool-use errors inside an agent loop?
	16.	What’s the toughest part about coordinating multiple agents?

MLOps / LLMOps
	17.	How would you detect and monitor hallucinations in production?
	18.	Which metrics matter most in an evaluation pipeline?
	19.	Walk me through deploying a GenAI service with FastAPI + Docker + K8s — how do you ensure rollback safety?

Scaling & Risk
	20.	If your system blows the budget because of model size, how do you decide on trade-offs — speed, accuracy, or cost?

MCP (Model Context Protocol)
	21.	What is MCP, and why does it matter for interoperability across GenAI tools?
	22.	How would you design a safe fallback if an MCP-enabled tool fails mid-workflow?

⸻

Self-test: Can you answer these clearly, without hand-waving? If not, that’s your prep gap.
Mini-project: Pick 5 → record 2-minute answers → rewatch. You’ll instantly spot where you sound credible vs vague.

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