Digital Factory AI Lead

Reshaping industry workflows by developing AI solutions and effective agentic systems.

Lead AI Engineer

moatless

Helping industry experts and researchers to navigate the sheer endless sea of Large Language Model (LLM) research & innovation because everybody deserves to find the best information.

Founder

Stealth

I was part of a 6 month government-funded Entrepreneurship program that enabled me to work on my own projects and ideas within the LLM space.

During this time, I gained experience in building LLM-powered backends with popular in-demand tools. The prototype I ended up building included the following:

- Fetching data from an external API & processing the response (Python)
- NoSQL database (MongoDB, Postgres)
- PDF Processing (PyPDF2, PyMuPDF)
- Document Extraction (Textract/Document Transformer)
- LLM Chain with multiple steps and prompt templates (Python, Guidance/LangChain)
- API endpoint for the processed data & the LLM outputs (FastAPI)
- Cloud Infrastruture (AWS)

Since I operated on a minimal budget, efficiency and cost reduction were a priority for me. Specifically, I focused on optimizing tasks like PDF processing, document understanding, information extraction and summarization.

Senior Machine Learning Engineer

A1 Telekom Austria AG

During my time as a Senior at A1, I was the DS lead for the company's most advanced NLP projects.

My work included

- Reporting results to several levels of management 
- Representing cutting edge Deep Learning & NLP at the company
- Pitching these topics to the C-Level and other influential exexutives
- Giving expert talks & presentations
- Participating in workshops
- Taking part in the recruiting process for full-time Data Science internships
- Guiding selected interns and assigning them roles in my projects

For a technical overview of my work, see below.

Senior Data Scientist

I was in charge of a Social Media Listening project for Facebook, Twitter and Online Newspapers which included the following

- SQL & MongoDB databases
- Web scraper for the newspaper(s)
- Processing Facebook & Twitter API data
- Transfer Learning & Language Model Finetuning on in-domain texts
- Multiclass and Multilabel Text Classifiers based on Embeddings and Transformers (the original PyTorch implementation of BERT)
- Tableau Dashboard

On top of that, I was developing intents & entities for customer text channels, including e-mail, web search, specialized chatbots (e.g., billing), the aforementioned Social Media data and several smaller Text & Sequence Classification projects.

I then moved on to explore speech channels and became the person in charge of Speech-to-Text at the company, which included 

- Processing short IVR audio recordings
- Processing long(er) call center recordings 
- Labeling pipeline with custom Prodigy scripts
- Developing a subtitle matching algorithm for aligning audio with text & filtering out errorenous subtitles
- Developing Hybrid HMM-TDNN Speech Recognition models with Kaldi
- Switching to (back then freshly released) bleeding edge libraries & models for End-to-end Deep Learning, such as NVIDIA NeMo & Facebook FAIR
- Improving performance on Austrian dialects & in-domain recordings
- Adapting the in-house Classification models mentioned earlier for the text output of the speech channels

In addition, I was able to work on PoCs in the areas of Document Extraction, Document Classification, Entity Extraction & Question Answering.

Data Scientist

Universität Wien

Research in Cognitive & Economic Psychology:

- Dual process theory (Type I/II thinking)
- Metacognition theory
- Experimental assessment of cognitive biases
- Leveraging aggregated mouse cursor movement data

Junior Researcher

Coursera

Data Science Education

As the founder of LLM Watch, my goal is to curate the most relevant Large Language Model (LLM) research and make it accessible to a broad audience. 

I distill complex technical topics into summaries that respect your time. 

Before embarking on this journey, I led Deep Learning R&D at one of Europe's largest telecom companies. There, I spearheaded numerous AI projects with a focus on Natural Language Processing (NLP) and Automatic Speech Recognition (ASR). 

With more than 6 years of experience in AI, I was there before the hype. 

I try to offer a healthy balance between shiny new things and what really matters.

Pascal Biese

Yann LeCun's first big paper after he left Meta. Here's what they did:

For years, training Joint-Embedding Predictive Architectures (JEPAs) has been more art than science. Stop-gradients, teacher-student networks, carefully tuned EMA schedules – a delicate dance of heuristics to prevent representation collapse. But what if there was a principled way?

Self-supervised learning promised to unlock the power of unlabeled data, yet state-of-the-art methods like DINOv2 and I-JEPA rely on complex recipes with dozens of hyperparameters. Each new dataset or architecture requires extensive tuning. Even worse, the training loss often doesn't correlate with downstream performance, making model selection nearly impossible without expensive labeled evaluation.

The core problem: we've lacked a theoretical understanding of what makes good representations. LeJEPA changes this by proving a fundamental result – isotropic Gaussian embeddings uniquely minimize worst-case risk across downstream tasks. 

Tho achieve this, they introduce SIGReg (Sketched Isotropic Gaussian Regularization), a novel objective that enforces this optimal distribution using random projections and characteristic function matching. The beauty lies in its simplicity: linear complexity, bounded gradients, and provably correct by construction.

LeJEPA eliminates stop-gradients and teacher-student networks entirely while achieving competitive performance – 79% on ImageNet-1k with a frozen ViT-H/14. And here's the best part: it works out-of-the-box across 60+ architectures and diverse domains - in-domain pretraining with LeJEPA on small datasets (even just 1,000 samples) outperforms transfer learning from massive frontier models trained on billions of images.

This could fundamentally shift how we think about foundation models. Instead of relying on ever-larger generic models, practitioners in specialized domains can now train competitive models directly on their data with minimal hyperparameter tuning. 

The fact that LeJEPA's training loss actually correlates 99% with downstream performance also solves the critical model selection problem that has plagued SSL research.

What do you think? Is Yann onto something?
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AI Lead at PwC <​/​> Daily AI highlights for 80k+ experts 📲🤗

LeJEPA: Provable and Scalable Self-Supervised Learning

Microsoft just announced the "Era of Agentic Organization". Here's what that means:

As LLMs scale their reasoning capabilities, we need new techniques: sequential thinking is too slow, and parallel thinking wastes compute by running independent traces that can't learn from each other mid-process. The industry needs a paradigm that combines the adaptivity of sequential reasoning with the speed of parallelization.

For this reason, Microsoft Research developed AsyncThink. AsyncThink is a new approach where a single LLM learns to play two roles simultaneously: an organizer that dynamically structures the reasoning process through Fork and Join operations, and workers that execute sub-queries concurrently. 

Their key contribution is that the model learns via reinforcement learning to optimize its own thinking structure - deciding when to split problems, which sub-queries to pursue, and when to merge intermediate results.

The results? 28% lower inference latency compared to parallel thinking while maintaining higher accuracy on mathematical reasoning. Models trained only on simple countdown problems successfully generalize their asynchronous thinking capabilities to completely unseen domains like Sudoku and complex math problems.

The more tasks AI is supposed to solve within an organization, the more important it becomes to structure all of this in a scalable and controllable way. AI agents need to learn to organize their reasoning like skilled project managers, adaptively coordinating multiple thinking processes based on the problem at hand. 
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The Era of Agentic Organization

Can we train billion-parameter models without gradients? Oxford researchers just showed it's possible - at 100x the speed.

Evolution Strategies have always been an elegant alternative to backpropagation. They don't need differentiability, handle noisy objectives gracefully, and parallelize beautifully. But there's been a fundamental problem: they simply don't scale to modern model sizes.

University of Oxford's new EGGROLL algorithm mirrors what made LoRA successful for fine-tuning - instead of perturbing all parameters with full-rank matrices, use low-rank approximations. For each population member, sample matrices A and B to form a rank-r perturbation AB^T rather than a full matrix E. This cuts memory from mn to r(m+n) per layer and reduces forward pass compute proportionally.

What makes this work is subtle but powerful: while each perturbation is low-rank, the final parameter update averages across the entire population, yielding a full-rank update when the population is large enough. The researchers prove this approximation converges to full-rank ES at an O(1/r) rate - faster than typical central limit theorem rates due to the symmetry of the problem.

The results validate the approach across three domains. In pure integer pretraining of recurrent language models, EGGROLL enables stable training with population sizes exceeding 250,000 - orders of magnitude larger than prior work. For LLM reasoning tasks, it matches or exceeds GRPO while maximizing hardware utilization. And critically, it achieves near-inference throughput speeds, making gradient-free optimization genuinely practical at scale.

This opens real possibilities for training systems that can't use backpropagation: end-to-end neurosymbolic architectures, models with discrete components, or deployment scenarios where integer-only computation is essential for efficiency.
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Evolution Strategies at the Hyperscale

We might be systematically underestimating LLMs.

We've been told that reinforcement learning is essential for frontier reasoning capabilities. Massive compute budgets, curated datasets, reward models - the whole apparatus. 

But new research from Harvard University challenges this expensive paradigm with a surprisingly simple question: can we just sample better from base models?

The paper introduces a training-free algorithm that achieves reasoning performance matching or exceeding GRPO (the standard RL method) across mathematics, coding, and science benchmarks. 

The key insight revolves around the "power distribution" - sampling from p^α instead of p. Unlike low-temperature sampling which greedily averages future token likelihoods, power sampling accounts for entire future completion paths. 

This seemingly subtle difference makes a big difference: it naturally upweights tokens with fewer but higher-likelihood futures, essentially planning ahead rather than getting trapped in dead-ends.

Using Metropolis-Hastings MCMC to approximate this distribution, the researchers show that Qwen2.5-Math-7B can reach 74.8% on MATH500 without any additional training - nearly matching the 78.5% from full RL posttraining. 

On out-of-domain tasks like HumanEval, it outperforms by up to 20 percentage points. Perhaps most remarkably, unlike RL which suffers from diversity collapse, this method maintains strong pass@k performance across multiple samples.

The takeaway is that base models may already contain the reasoning capabilities we're spending enormous resources to extract through RL. This would open up a new axis for inference-time compute scaling that's accessible to anyone with a base model - no training infrastructure, curated datasets, or verifiers required.

We're still learning how to properly query the "intelligence" that already exists in our models.
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Your Base Model is Smarter Than You Think

Kloudstac

Kloudstac is a AGI (Artificial General Intelligence) Acceleration Platform startup born in the ChatGPT era, helping Fortune 2000 enterprises in improving adoption & value realization from AGI via our learn & build playgrounds and use case pilot accelerators.

We are the World's leading provider of high quality AI talent transformation solutions (as part of 1st leg of our AGI Acceleration agenda) incl largest portfolio of AI upskilling programs (30+) in the Industry and 500+ labs/ playgrounds across personas in Generative and Agentic AI. 

Our unique approach offers Enterprises a Holistic, Continuous, Accelerated & scalable AI Talent Transformation solution that is unmatched in the Industry.

Delivered over 150 Generative and Agentic AI programs in last 2.5 yrs. We partner with Fortune 2000 Enterprises and top 30 leading Edtech/ Corporate and B2C education providers globally (across 5 countries) for servicing their customers on AGI/ Agentic & Generative AI talent development. 

Our offerings:
1. AGI Playground platform with learn, build labs & personalized AI tutor supporting each learner in their AGI skill development journey, coupled with our Agentic AI Copilots/ Pilot Accelerators
2. Agentic AI Industry Solutions and AGI Strategy & Value discovery (use cases & pilots, implementations)- our consulting services
3. 30+ AGI / Agentic AI training programs for B2B, B2C - across technologies, Industries, Business functions

Website: https://www.kloudstac.com

My specialization:
1. Mentor/ Advisor (AI/AGI) for Business & Technology Leaders across Industries, GenAI/ Agentic AI Architect programs across AWS, GCP, Azure, OCI (delivered ~100 batches till Jun 2025)
2. AI Use cases incl AI Agents across Industries, horizontals incl frameworks, use case discovery, detailing
3. Architecture & development- RAG, AI Agents, Finetuning: OpenAI, Anthropic, Google, Microsoft, NVIDIA, Mistal, Meta stack
4. LLMOps & MLOps- GCP Vertex AI, AWS Bedrock & Sagemaker, Azure AI Studio

CEO & Product GTM Leader, Generative/Agentic AI/AGI Advisor & Skills Trainer

Globussoft Technology

Building Generative AI (GPT2/3.5) driven content generation app.
Responsible for business-as-usual/ ops, re-architecting/ modernizing, new features, improvements, growing revenue on 3 SAAS products in digital marketing, adtech, workforce productivity & engagement (empmonitor.com, poweradspy.com, socinator.com).
Tech stack: GCP- MongoDB, Firestore, cloud functions, GCS, Bigquery, GKE-Kubernetes, Compute- VMs, containers, docker, Cloudsql-RDS, Elasticsearch, APIGEE; AWS- S3, EC2, Lambda, RDS-mysql, documentdb, API Gateway, athena; NextJS, NodeJS, Polygon L2 scaling with solidity-ethereum
Achievements:
1. Restructured product organization, cutting 30% cost, optimized engg team. This includes reducing cloud costs by 70-80% in 8 months. 
2. As architect, re-engineered/ created to-be design for EMPMonitor.com on AWS, later on GCP and poweradspy.com on GCP. Used best-in-class tech stack - serverless and containerized microservices.
3. Changed/ modernized the obsolete/in-consistent product strategies for EMPMonitor.com (employee monitoring to Workforce productivity & engagement platform), poweradspy (updated to- "Discover social media ads + content strategy of competitors") with 18 months product roadmap in line with market reqs/in-demand use cases.
4. Improved processes & practices/mix across sales, marketing, ops leveraging past experience in startups, large enterprises. Cut down marketing costs by half within 3 months.
5. Executed ISV partnership with Google cloud platform giving global access to customers and GCP sales network via GCP Marketplace
6. Defined, optimized product roadmaps, pricing & GTM strategy on continuous basis and provided needed leadership & mentorship to various teams.

Chief Product & Technology Officer

Skills: Product Design - UI/UX, business workflows, use case development
Product Engg: AWS, GCP - OLTP, OLAP backends; AI - NLP, CV, Machine teaching; Data engg: Snowflake, Databricks; Web3.0/ Blockchain: Polygon, Solana

Helping tech startups on their product journeys:

As Product advisor:
1. Business model & product design- Edtech startup focused on rightskilling engg students
2. LiveCommerce startup on product platform & tech design
3. Design- Fintech BNPL (Buy now pay later) platform

Tech/ Engg Advisor:
1. HR Tech co - App platform (Azure) architecture & design assessment, improvement, new capabilities
2. Edtech platform- MVP development
3. App platform (AWS) architecture & design assessment for a Travel tech co
4. Solutioning for "machine operator automation" use case using Deep RL, Machine teaching for manufacturing.

Startup Advisor- Product & Technology

GoBaskt Inc

GoBaskt (US headquartered, http://gobaskt.com) is a tech product startup offering a hyperlocal commerce platform for local businesses, retailers & CPG Brands. We are helping businesses acquire & retain customers and improve revenue within local area of 5-6 miles with our unique technology.
For local businesses, we bring modern technology, that only large brands can afford, on a value based pricing model.

For consumers, we offer an intelligent virtual shopping assistant app that uses AI, multi channel engagement, analytics in new ways, in order to create unique interaction experiences between individuals and businesses for commerce, marketing and service.  It helps consumers to save time, money & efforts, while shopping for services & products.

Achievements:
1. Developed product vision-mission, value proposition, use cases, UI-UX/ business workflows, pivoted to more valuable use cases during covid times and built them fast.

2. Built technology strategy, designed, developed, deployed complex full stack web and mobile apps  with NextJS-React for web apps (migrated from angular), backend with python-nodejs (migrated from Java) + AWS Lambda, Containerized microservices with AWS Fargate/ ECS and EKS/ Kubernetes, Snowflake, S3, Athena, Kinesis/ Kafka, AWS Step functions, mobile apps with IOS native, Android native (Kotlin), databases- dynamodb, mongodb, postgresql, DevOps- Github-AWS Codebuild, codepipeline, codedeploy. Multiple use case built and deployed in production over 2 yrs.

3. Ran engineering/ operations frugally with a low budget esp during COVID time when primary customer market got disrupted. Bought revenue/ sales.

Co-Founder, Chief Product & Technology Officer

TheCyborgs

1. Academic level program to enable emerging tech skill development via high quality hands-on projects and mentorship from experienced professionals. So far, 30+ students across few engineering institutions benefited from the program.

2. Global virtual "Artificial Intelligence" Professional Community comprising 150+ AI practitioners, with the aim of mastering concepts, industry use cases, code implementation and practical hands-on innovation project based application of emerging AI techniques. 

Focus areas- Computer Vision, NLP, Predictive/ Prescriptive/ Descriptive analytics, AWS & Azure, Chat & voice bots, Web & mobile apps, Augmented Reality.

Sample recent topics (AI):
1. Transfer learning for NLP with Google BERT
2. LSTMs, GRUs, Attention models & transformer architecture
3. Capsule nets
4. GAN Implementations
5. Yolo v3 object detection
6. Deepspeech architecture, CTC, Attention models for ASR
7. Siamese Networks
8. Brain Computer Interfaces
9. Deep Reinforcement learning with OpenAI, Tensorflow
10. Recommendation models

Founder (Artificial Intelligence Tech Research & Innovation Community- Industry-Academia)

Kore.ai

25+ engagements (POCs/ pilots/ implementations/ running large programs) (CPG, Telcos, Banks) as program/project mgr, BA, Solution architect (includes pre-sales work). Clients- Loreal, Verizon, Citibank, Deutsche Bank, Philips Lighting, BB&T Bank, Nestle, P&G, Yahoo Sports, ABInBev, Colgate, etc.

Built Professional Services/ Delivery organization for North America- Sept 2017 to Jun 2018. 

Managed solution design, development & delivery in multiple strategic customers- bot solutions roadmap, use cases discovery/ new projects/ generate additional business, help improve operational processes & capabilities of deployed bot solutions.
Hands-on bot building on Kore, facebook, amazon alexa, google assistant, IOS & Android apps.
Hands-on/ deep expertise on AI engines in production: NLP- Supervised & unsupervised ML, Computational Linguistics/ Fundamental meaning, Knowledge graph.
Expertise in discovering/ proactively developing 20+ net new use cases.

As part of Product Mgt (Monetization, New Products), my responsibilities:

1. Executing services (delivery, solution design, dev, deploy)- POV Pilots, Implementations and improving customer satisfaction & value creation from using kore products
2. supporting license sales cycles & proposals as functional and product SME/ pre-sales architect
3. working with product management on new products (idea-to-MVP), product improvements/ bug fixes/ enhancements/ new functionalities (includes providing detailed requirement, conceptualizing user stories).
4. content creation- blogs, articles, case studies, etc
5. contributing to SI and OEM alliance development as SME
6. Conceptualized, prototyped new product ideas.
7. demos- industry use cases, developing day-in-the-life scenarios, conversation flows.
8. setting up/ optimizing chatbot programs (covering entire bot lifecycle) in large enterprises
9. Work with product engg daily/weekly on technical issues/ bugs, limitations/ new technical features.

Director- Outbound Product Management

Capgemini

Key focus areas (Digital Transformation initiatives- practice sales, advisory, delivery, IP dev): 
1. AI/ Cognitive & Intelligent Automation- chat & voice bots, conversational analytics & NLG, deep learning, computer vision, NLP based automation (OCR, intent & entity extraction from email text)
2. Big data analytics- SPARK, R, python, HADOOP HDFS
3. Industrial & Consumer IOT (Device & Sensor data mgt, IOT protocols, Edge & Cloud analytics, algorithms, real time operational dashboards, KPIs/ metrics, asset maintenance& monitoring )
4. Blockchain technology driven Industry solutions for product supply chain based sectors- CPG, Retail, Pharma, O&G
5. CXO Cockpit/ Digital Boardroom analytics - 360 view of corporate performance & key driver analysis

Partial hands-on exposure to 
a) Big data technologies:  SPARK, NoSQL DB - MongoDB
b) Statistical and Machine learning tools/ languages with R, Python (Scikit learn, numpy, pandas)
c) Blockchain tools @ Ethereum
d) Conversational apps with Enterprise AI Platforms- chatbots/ virtual intelligent assistants
e) Formerly- SAP HANA, SAP BPC, SAP BI, Analysis Office & BOBJ Cloud
f) Cognitive/ ML APIs- Google cloud platform, Microsoft Azure, IBM Watson
g)  Deep Learning with Google Tensorflow

SME on emerging solution spaces in Digital Marketing & Marketing driven commerce- Account Based Marketing, Personalized & contextual multi-channel multi-wave campaigns, dynamic customer profile, segmentation, predictive analytics, social listening, location based marketing, mobile loyalty programs, use of AI/Cognitive technologies for Digital Transformation across front office & back office business processes and business model re-configurations in select industries.

Hands-on exposure on AI frontiers- Meta learning, Siamese networks, Generative adversial networks. GAN, GRUs & LSTM, etc.

Senior Manager, Emerging Digital Technologies (Insights & Data)

Developed global SAP HANA Practice with GTM collaterals/assets, POVs, service offerings, case studies, solution accelerators, relationships with SAP Labs & SAP/ Hybris Field Sales.

Managed HANA requirements (solutioning, proposals) on BI-Native HANA, BW-HANA, BPC-HANA, S/4HANA, Fiori/UX, HANA Cloud, Hybris Marketing across 20+ customers & prospects.

Drove demand generation programs on HANA (new pursuits & account mining initiatives) with sector sales leaders.

Key contributions/achievements:
1. Achieved 500% growth in HANA SMEs headcount in 1.2 yrs.
2. Built 23+ solution prototypes/ demos on HANA across applications
3. Achieved approx. 800% growth in HANA Case studies in 1.2 yrs.
4. Achieved 270% growth in revenue from HANA Services in 1.2 yrs. 
5. Delivered multiple HANA Engagements- Implementation, Pilot, Advisory successfully.
6. Developed 30+ HANA Collaterals- capability decks, reference architectures, POVs, solution models, etc
7. Built multiple new HANA Cloud alliances as part of building HANA Cloud practice and developed highly differentiated joint service offerings.

SAP HANA Practice & GTM Head, North America

ITC Infotech USA Inc

Worked with Sales Teams, Partners and customers/ prospects on project support/delivery oversight, proposal/ requirement solutioning in Business Analytics & EPM. Applications include - BPC, BW, HANA- Analytics. Identification & development of various sales channels, drive capability development initiatives.

Built competencies on Consolidation/ Group Close based on IFRS Standards (IAS 27, 28, IFRS 3,10,11)

Managed lead generation & deal pipeline in North America on SAP BPC, BW, BusinessObjects, HANA by working with Geo Sales, Partners and Inside Sales.

Oversee delivery/ solutioning for offshore projects on BPC/ BPC-BW for North American Customers.

Developed well rounded exposure in BPC - Planning : Sales, Material, Production/ S&OP, Direct & Indirect Costs including HR Headcount & payroll cost, CAPEX, Balance Sheet, Income Stmt, Cash Flow Stmt, Machine level Capacity Planning, Plan data retraction to ECC, FICO Processes. Discrete & Process Manufacturing.

Bought improvements in Sales Process for SAP Business Analytics & EPM in following areas:
1. Prospecting, Lead Generation, Sales Campaigns
2. Initial Engagement and Positioning Services/ Value Offerings

Driving Practice building & Capability Dev. in BusinessObjects Reporting and Advanced Analytics (Descriptive/ Predictive/ Prescriptive on Big Data) on SAP Predictive Analysis/ Infinite Insight, HANA, R-HANA-HADOOP Integration, SAP's Advanced Analytics RDSes for Sales, Marketing, Manufacturing, Retail/ CPG

PreSales & Solution Delivery Head - SAP Business Analytics & HANA, North America

- Built BPC and HANA Practice since inception at ITC Infotech. Developed SAP BPC and HANA In-Memory Analytics Capabilities, Offerings, New Solutions, GTM. Work on PreSales, Implementations, Consulting Engagements including True Profitability/ Costing, Shared Services Costing, Driver based Financial Planning
- PreSales on SCPM-HANA, Demand Signal Management on HANA
- Evolved Hub-n-spoke model of HANA Practice, did internal selling and put into practice
- Expanded sales channels and utilized them for lead generation

Capabilities Developed
1. BW and BPC Database Migration to HANA
2. HANA COPA Accelerator
3. HANA Demand Signal Management
4. HANA Information Models/ Custom Data-mart and consumption into BW data models
5. HANA Value Assessment and Proof of Value Consulting Offering

Demo Solutions designed and executed/ in-progress on HANA:
1. Simple and Complex Reporting Scenarios with high data volume
2. Mixed Scenarios – Consuming HANA Information models in BW and vice versa
3. Real Time Reports and Dashboards using SLT Replication in HANA Information Models
4. Data Extraction into HANA using DXC (Direct Extractor Connection)
5. Inventory Reports & Dashboards on BOBJ-HANA with 3 million records, performance comparison with similar scenario on BW – Traditional Database
6. Market Research and POS data Analytics in Demand Signal Management-HANA

Practice & PreSales Head - SAP BPC, HANA Solutions

Philips Electronics Limited India

Working as a SME in Supply Chain Planning/APO and Finance ( Profitability and Cost Analytics) for SAP BW. Was part of BW projects on Supply Chain Planning, Profitability & Cost Mgmt, took several handovers of several projects during transition to Support, Managed Team for delivery on Incidents and Data Load Monitoring, Involve in Technical Incident Resolution and Data Load Monitoring, Knowledge Management and Training Program Dev. for SAP BW Team.

Developed Functional knowledge on supply chain planning and dimensional profitability & cost allocation mechanisms.

Sr Technical Lead/ SME for Supply Chain Reporting & Analysis

Accenture

SAP Business Intelligence Team Lead, & Techno-Functional Consultant, Functional SAP FICO. MM Consultant, BusinessObjects Reporting & Analysis

SAP Business Intelligence Consultant and Team Lead

21+ yrs of experience in B2B and B2C Software Product Lifecycle (SAAS, On-premise/ run anywhere)- Ideation, MVP, Post revenue/ growth stage, late stage, rearchitecting/ modernizing, high scale (retail, telco) & multi-country complex production environments, customer implementations, customer onboarding, presales/ sales support, product roadmap & release mgt, multi-product operations, product design (UI-UX), product analytics incl customer journey mapping.

10+ yrs in AI/ML incl 4 yrs in Generative AI, 2 yrs in Agentic AI & RAG. 35+ AI Projects delivered as Architect incl 15+ production deployment, part of engineering/ product mgt in 4 AI powered products.

Worked on products with peak load stats: MAU of upto 3 mil users, 40-50k DAUs, 1000-1500 transactions per sec (tps).
Max team sizes handled - 300

Core Experience :

1. Agentic & Generative AI/ Artificial General Intelligence (3 yrs)- LLMs, Vector databases, Advanced design patterns & strategies- AI Agent development, AWS Bedrock, GCP Vertex AI, Azure AI Studio, Advanced RAG techniques, evaluation & accuracy improvement, finetuning- Supervised, RL.

2. Classic ML- Supervised, unsupervised learning
3. Data Engg & Analytics- Google Bigquery, Dataflow, Cloud composer (Airflow), Spark, Kafka
4. Cloud Infra & DevOps (AWS, GCP, Azure)

Domains- 
Supply Chain: S&OP/ IBP, New product introduction, demand sensing & shaping, O2C, supply chain control tower, SCOR, MRP, Master prod scheduling & RCCP, P2P, MD, asset ops.
Customer experience (Marketing, Sales, Service): multi-channel commerce, Customer 360, journey mapping, campaign, loyalty, planning, analytics (attribution, ROI, churn, market basket analysis), recommendations, lifetime value

FP&A: Planning/ Budgeting/ Forecasting, analytics (profitability, balance sheet, cash flow, EVA/ Economic profit, value creation), subsidiary & group close/ consolidation, activity based costing, MD.

Industries: Retail/Ecom, CPG, O&G, BFSI

8 yrs Entrepreneurial Experience: co-founder/ partner in 5 startups (past, current).
Product & CTO advisor to multiple tech startups.

taplio

The Battle

Pascal Biese

Abhiroop Roy

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The Battle

Pascal Biese

Abhiroop Roy

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Pascal Biese's LinkedIn Performance

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