Large Scale Inclusive (Less Biased AI) Images Classification (Sub-Project: Universal Vision)
Arunkumar Venkataramanan
About
Projects
Experience
Skills
Education
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Arunkumar Venkataramanan is a founder-executive and research-driven technologist working at the frontier of artificial intelligence, where intelligence transitions from representation to action. His work centers on a single, difficult problem: how to build AI systems that can reason, decide, and operate reliably in the real world as autonomy increases.
He is the Founder, CEO, and Chief Product & Technology Architect of DeepBrainz AI & Labs, a DPIIT-recognized AI organization structured as a unified research-to-production foundry. The company spans enterprise and consumer technology and is designed to collapse the distance between theory and consequence—forcing every research idea to survive deployment, real users, incentives, latency, and failure.
Within DeepBrainz AI & Labs, Arunkumar leads the development of reasoning-first models, agentic AI systems, and world-model–driven platforms, including initiatives such as DeepBrainz-R and Lexopedia AI. His approach rejects surface-level fluency and scale-first thinking in favor of systems that exhibit judgment, planning, and stability under uncertainty.
Over more than a decade in the technology industry, Arunkumar has operated across the full lifecycle of AI systems—from foundational modeling and training to inference optimization, infrastructure, and real-world deployment. He has founded and co-founded multiple startups across enterprise SaaS and consumer technology, repeatedly encountering where elegant theory fractures under operational pressure.
His technical background spans modern AI paradigms including large and small language models, transformers, mixture-of-experts systems, diffusion models, hybrid and agentic architectures, alignment and preference optimization, and large-scale ML systems. Equally central to his work is production discipline: MLOps, evaluation under distribution shift, cost-aware inference, and edge-to-cloud deployment.
Arunkumar treats alignment as a structural property of systems, not a post-hoc policy layer. As autonomy increases, he views interpretability, steerability, and predictable degradation as non-negotiable design constraints. Intelligence that cannot be understood, corrected, or trusted under stress is not intelligence worth deploying at scale.
His thinking is shaped by decision theory, cognitive science, systems engineering, and philosophy, which inform how intelligent systems should act under uncertainty and conflicting objectives. These influences are not academic interests but operational inputs to how systems are designed, evaluated, and shipped.
In parallel with building technology, Arunkumar has contributed to and engaged deeply with the global AI ecosystem. He has participated in research and practitioner communities, collaborated with cloud and AI platform partners, and earned recognition across industry and research circles. He has also mentored founders, engineers, and researchers with a focus on long-term thinking, technical rigor, and execution discipline.
Arunkumar builds with long time horizons and low tolerance for performative progress. He prioritizes clarity over hype, execution over signaling, and irreversible commitments over optionality. Once a direction is chosen, noise is ignored and leverage is compounded deliberately.
He does not view this work as aspirational. He views it as an obligation imposed by capability. As intelligent systems become more autonomous and more embedded in society, the cost of misalignment grows faster than the benefits of raw performance. Avoiding responsibility is no longer a viable position.
Arunkumar Venkataramanan’s mission is precise and uncompromising: to build intelligence systems that can reason, act, and remain aligned as they scale—creating durable leverage for humanity in a world where autonomy is no longer optional.
His LinkedIn profile serves as a historical curriculum vitae; this page reflects his current operating philosophy and long-horizon work.
Reach Out At
Work Email
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: arunkumar.ramanan
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Large Scale Video Understanding and Classification (Sub-Project: Universal Vision)
Large-scale Multi-label and Multiclass Visual Recognition (Sub-Project: Universal Vision)
Large Scale Instance Level Recognition and Retrieval (Sub-Project: Universal Vision)
Project: Automated Speech and Sound Recognition with Pre-trained Models
Targeted Digital Ad Ranking, Ads Demand, Clicks, CTR, and Click Fraud (Detection) Prediction (Sub-Project)
Project: Demand Prediction with Time Series Forecasting
Generative Language Models and Deep RL for Universal Lang (Sub-Project)
Universal Bot Sub-Project: Generative Deep Learning for Universal Language as Conversational AI
Universal Vision Project: Large-Scale Inclusive and Multi-Modal Visual Recognition with Deep Learning
Universal AI Project: Enterprise AI with Distributed Deep Learning at Scale
Project: ML-based Hybrid Recommender Engine for Digital Ads
Universal Language Project: with Generative LLMs and Deep RL
Project: Next-Gen AI Platform - Unlocking Sustainable Growth and Streamlined Business Processes
Project: Enterprise SaaS Platform's New Launch with Seamless CRM and ERP Integration via API for User Experience
Project: Cloud-Based Data Analytics Platform
E-commerce Platform Product Improvement Project
Project: Social Media App Improvement for Mobile and Web
FinTech Product Improvement Project
Project: Smart Home Assistant App Platform
Project: Marketplace for Creators (Gig) Economy
The Product Thinking Playbook: A Thought Leadership Series on Product Management in Tech
Achieving Product Success: Steps to Successfully Launching a New Product
Embracing Lean and Agile Methodologies for Product Success: A Guide to DevOps in Tech Product Management
Preventing Product Failure: Avoiding the Most Common Product Management Mistakes
The Future of Product Management: How to Balance Emerging Trends and Technologies with Immediate Challenges and Opportunities
The Product Manager’s Guide to Market Research for Better Business Strategy: Creating Products for Your Target Audience with Segmentation
Effective User Interviews: Valuable Insights for Product Success
Navigating Product Management in the Post-Pandemic World: Adapting to Changing Customer Needs and Market Trends
Driving Digital Transformation for Business Success: The Critical Role of Product Management in Tech
Product Management for Startups: Navigating the Challenges of Early-Stage Product Development
Leveraging Big Data for Product Innovation: Creating Better Products with Data Science
The Power of Data Analytics in Product Management: Making Better Product Decisions with Data
Building Better Products with Blockchain: The Role of Blockchain in Product Management
Top Product Management Tools: Essential Resources for Product Managers
Driving Success with a Winning Product Strategy: Developing a Strong Product Strategy
Product Thinking for World-Changing Products: Innovating with Purpose
Achieving Product-Market Fit: Building Products People Want
Getting Your Product to Market: Strategies for Winning Product Roadmaps
The Power of Customer-Centric Product Development: Balancing Innovation and Execution in Product Management
The Importance of User Research in Product Development for Better User Experience: Meeting User Needs
Incorporating Design Thinking in Product Management: Building Better Products with Design Thinking
The Art of Prioritization in Product Management: Making Tough Product Decisions
Leading Successful Cross-Functional Teams: Strategies for Cross-Functional Team Management in Tech
Improving Your Product with Data: Making Data-Driven Decisions in Product Management
Managing Stakeholder Expectations: A Guide to Stakeholder Management for Product Success
Building Strong Relationships with Engineering Teams: Working with Engineers in Product Management
Creating Winning Product and Engineering Teams: Building Technology or Products People Love
The Product Manager's Guide to Artificial Intelligence
5 Ways to Validate Your Product Idea (Blog Post)
Cloud Technology Essentials for Product Managers (Article)
Founder, CEO, Chief Technologist and Product Leader Nov 2019 – Present (3 yrs 6 mos)
DeepBrainz AI, Bengaluru, India (Hybrid)
As Early Stage Tech Startup Founder, CEO, Chief Technologist, and Product Management Leader at DeepBrainz AI:
- Launched AI/ML technologies and SaaS/Cloud products for enterprise customers, resulting in 35% revenue growth.
- Drove product vision, go-to-market strategy, and positioning, resulting in a 25% customer adoption increase. Partnered with cross-functional teams to deliver the next phase of cloud services, reducing development time by 40%.
- Developed innovative product solutions and increased market potential by 30% through collaboration. Achieved a 20% increase in customer satisfaction and retention by understanding the AI ecosystem, markets, competition, and user requirements.
- Successfully launched new products and features, increasing customer engagement by 15%.
- Collaborated across teams to guide products from conception to launch, improving overall product quality by 25%.
- Leveraged generative, prescriptive, and predictive AI to make accurate product decisions.
- Created a novel smart data fabric and AI/ML pipeline for ModelOps and MLOps of AI cloud, reducing operational costs by 30%.
- Built predictive models for business-critical use cases, resulting in a 15% increase in customer demand.
- Compared learned model performances with suitable metrics, improving model accuracy by 10%.
- Prototyped, tested, and iterated on cloud AI minimum viable products, reducing time to market by 20%.
Co-founder Apr 2022 – Present (1 yr mos)
Stealth mode startup, Bengaluru, India (Remote)
As Co-Founder of a Stealth Startup since Apr 2022:
- Leading a Consumer Tech startup company in stealth mode with Engineering and Product Leadership, focusing on building innovative products and exceptional customer experiences for emerging consumer markets across the verticals or industry.
- Building a consumer Super App product in stealth mode that incorporates the following verticals or industry, domains or technologies as a single consumer tech platform product: E-commerce, Marketplace, Mobile and Web, Social, Consumer (Smart Internet) AI, Fintech, More.
Founder Jun 2016 – Oct 2019 (3 yrs 5 mos)
Stealth mode startup, Bengaluru, India (Remote)
As a stealth startup founder
- Led 3.5 years of cutting-edge AGI R&D based on Deep Learning, Deep RL, Automated ML, Computer Vision, NLP, and Generative AI.
- Pivoted later from AI/ML Research to AI/ML Production and aligned DeepBrainz AI Cloud after the private preview product launch to meet market demand and incorporated DeepBrainz Technologies Pvt Ltd in Nov 2019.
- Launched a next-gen AI cloud platform for mid-to-large enterprises with a purpose-built no-code strategy-first approach and edge-to-cloud real-time AI/ML infra services.
- Empowered users to source data, build, deploy, and operationalize models with ROI optimized cloud AutoML and ModelOps (with MLOps, XAI, Edge AI) without deep tech expertise.
- Achieved 10X reduction in cost and time-to-market while accelerating AI adoption, democratizing AI in the enterprise, and driving revenue growth and innovation.
- Contributed to creating an innovative product with significant impact across industries.
Tech Product Management Consultant & Architect - Independent (Data & AI | SaaS & API | Cloud & Mobile) Jul 2015 – Sep 2019 (4 yrs 3 mos)
Independent Software Product Development Contracts, Bengaluru, India (Hybrid)
As an Independent Tech Product Management Consultant (Data, AI/ML SaaS/API, Cloud/Mobile) for Independent Software Product Development Contracts:
- Influenced product strategy by identifying opportunities for automation and optimization using ML, guiding product decisions, and influencing the product roadmap, resulting in a 30% increase in revenue and a 20% improvement in customer satisfaction for clients.
- Managed individual project priorities, deadlines, and deliverables, resulting in on-time and on-budget project delivery for 10+ projects.
- Championed the use of software engineering standards, practices, and approaches, resulting in high-quality software products that received a 95%+ client satisfaction rating.
- Developed, deployed, and maintained Machine Learning models and infrastructure that resulted in a 20% increase in revenue and a 15% improvement in customer satisfaction for clients.
- Led the design and implementation of Large Scale Machine Learning (Deep Learning) Distributed Systems that enabled clients to scale their ML applications by up to 50%, while reducing costs by 25% and improving performance by 30%.
- Architected and implemented end-to-end ML solutions for clients, resulting in a 25% improvement in accuracy and efficiency of their ML applications.
- Applied Neural Architecture Search (NAS) and other ML technologies to build efficient and scalable distributed ML systems, resulting in a 40% reduction in infrastructure costs and a 20% improvement in speed and reliability of ML applications.
Tech Product Development Consultant - Independent (Data, AI/ML, SaaS/API) | Kaggle Master (DSML) Mar 2016 – Apr 2019 (3 yrs 2 mos)
Independent, Bengaluru, India (Hybrid)
As a Technical Product Development Consultant - Data & AI/ML (Independent), Kaggle Master (Competitive DSML), Cloud AI (Enterprise SaaS/API) Consultant,
- Achieved the rank of Top 1% (ranked 38/102k) and Kaggle Kernels Master ranked 49th among over 100,000 participants in Kaggle's Machine Learning competitions and Kernels from March 2016 - March 2019.
- Ranked in the top 0.2% (ranked 224/164k) as a Kaggle Competitions Expert, winning medals for ranking 74th/201 in Google AI's Open Images 2019 - Visual Relationship, 90th/521 in iMet Collection 2019 - FGVC6, and 62nd/465 in Google AI's Inclusive Images Challenge on Kaggle.
- Demonstrated expertise in Python, Java, and C/C++ with working experience in Technical Program Management, also by guiding students'community as a mentor on coding and tech product development best practices like PLC, SDLC, versioning and debugging tasks.
- Passionate about staying up-to-date with state-of-the-art techniques and interacting with developers, engineers and data scientists on discussion forums.
Technical Program Manager (Independent Consultant) Jun 2013 – April 2016 (2 yrs 11 mos)
Independent Technical Consulting, Bengaluru, India (Hybrid)
As Independent Technical Program Manager, Sr. Technical Project Lead, Software Engineer and Architect, Big Data & ML Consultant for Independent Software Development Project Contracts (Independent Consultant - Big Data and Machine Learning),
- Contributed to 500+ GitHub projects, with 40+ pull requests merged and overall project quality improvement of 30%.
- Built and deployed 5+ open-source software solutions using AI/ML, achieving 50% improvement in system performance.
- Collaborated with 10+ developers and stakeholders to solve large-scale real-world problems.
- Developed and evaluated 5+ novel Machine Learning models for Computer Vision and NLP, with 10% accuracy improvement on average.
- Completed 15+ freelance projects on popular platforms with 95% client satisfaction and 4.8/5 average rating.
- Designed and implemented 10+ Machine Learning models, resulting in a 40% improvement in system performance.
- Worked with 20+ clients to deliver customized solutions that met their unique needs.
- Delivered state-of-the-art big data and machine learning solutions, increasing efficiency by 30%.
- Used Predictive Analytics and Modeling to address business problems, with a 25% product performance improvement.
- Analyzed data and created dashboards to monitor success metrics, reducing data analysis time by 20%.
- Conducted experiments using predictive analytics, resulting in a 15% increase in product adoption rates.
- Developed and deployed statistical models, algorithms, and segmentations, leading to significant improvements in performance metrics and 20% increase in customer satisfaction.
- Overall, my work as an Independent Big Data & ML Consultant and Software Architect added significant value to the organizations I worked with and contributed to their success.
Product Skills and Knowledge
Leadership
Project Management
Product Vision
Product Strategy
Product Roadmap
Product Design
PRD
Requirement Gathering
MVP
Agile
Product Lifecycle
Customer Development
UX
User Persona
Wireframing
Usability Testing
Analytics
Product Metrics
A/B Testing
Market Research
Market Sizing
Product Positioning
Competitor Analysis
Product Thinking
Prioritization
Product Launch
Customer Feedback
Funnel & Cohort Analysis
GTM
Communication
OKRs
Growth Hacking
Empathy
Product Pricing
Stakeholder Management
Product Tools and Technologies - I
Product tools
Product Manager tools
PM tools
Product tools
Figma
Sketch
Amplitude
Invision
Balsamiq
Trello
Product Tools and Technologies - II
Atlassian Jira
Asana
Microsoft 365
Coda
Google Analytics
Hotjar
Salesforce
Segment
Google Workspace
User Testing
Adobe XD
Confluence
Miro
Mixpanel
Optimizely
Mural
Notion
Pendo
Conferencing tool
Programming Languages
Python
Java
C++
C
Go
JavaScript
Bash
SQL
Libraries and Frameworks
PyTorch
TensorFlow
Keras
XGBoost
Pandas
scikit-learn
NumPy
Matplotlib
GPT-3
Spark, Kafka & Beam
MLflow
Kubeflow & TFX
Engineering Tools and Technologies
Visual Studio
Git
Ubuntu
Linux
Kubernetes
Jupyter Notebook
flask
PyCharm
Cloud Services
PostgreSQL v MySQL
MongoDb v Cassandra
Continuing Education (CEU) and Learning July 2013 – Present (~10 yrs)
Independent Coursework and MOOCs - Continuing Professional Education
Engineering and Technology Product Management and Leadership (Coursework & MOOC)
For more information about his projects and work experience, please refer to his LinkedIn profile for more information.
Bachelor of Technology (B.Tech), Information Technology (a Regular Engineering Degree) Aug 2009 – June 2013 (4 yrs)
Anna University, Chennai, India
(B.Tech Regular Coursework and Project work):
Furthermore, for more detailed information on his education, skills, knowledge, projects, and work experience, refer to his LinkedIn profile, including the Experience and Projects tabs.