How did I transition from Applied AI research to AI Consulting?
My transition from AI research to consulting evolved through several strategic phases:

Industry Experience Foundation
I first built credibility through high-impact roles at big tech and leading startups:
  • AI Research Scientist at Amazon Alexa AI, working on speech recognition and NLP
  • Led AI teams at Swiggy, developing various NLP and speech technologies
  • Headed AI team at Docsumo, managing 25+ ML and Data engineers

Consulting Motivation
Two primary factors drove my move to consulting:
  • Desire to leverage unique expertise in both neuroscience and artificial intelligence
  • Interest in helping early-stage startups develop appropriate Data and AI strategies

Consulting Focus Areas
My consulting practice encompasses:
  • Generative & Traditional AI development strategy
  • Corporate AI training and development
  • Professional coaching for Generative AI, NLP, and Deep Learning
  • Expert technical content creation for AI startups

Impact and Scale
As an AI consultant, I have achieved significant results:
  • Helped organizations save substantial costs and development time
  • Guided in-house AI/Tech teams in planning and deploying AI applications
  • Worked with companies across India, USA, UK, and Europe
  • Mentored over 100 professionals who secured positions at companies like Apple, Meta, and Amazon

My diverse experience across academia, big tech, and startups helped me establish myself as an independent AI consultant.
What unique approaches do I bring to AI Consulting?
I bring several distinctive approaches to AI consulting that stem from my unique background:

I. Interdisciplinary Expertise

Neural Networks Understanding
  • Deep knowledge of both biological neural networks from neuroscience research
  • Practical experience implementing artificial neural networks at scale
  • Ability to translate brain-inspired algorithms into real-world AI applications

II. Consulting Framework

Strategic Assessment
  • Systematic identification of AI use cases specific to industry domains
  • Validation of internal data assets for feasibility and business impact
  • Development of detailed implementation roadmaps and prioritization frameworks

Implementation Approach
  • Hands-on collaboration through weekly online calls
  • On-site visits lasting 1-3 days monthly for project-specific consultation
  • Extended 1-2 week monthly visits for strategic consultation roles

III. Industry-Specific Solutions

Specialized Focus Areas
  • LLM implementation for B2B e-commerce
  • Domain-specific LLM fine-tuning for enterprises
  • Generative AI pricing and monetization strategies
  • Machine learning infrastructure development

Organizational Development
  • AI team building and upskilling programs
  • Cross-functional stakeholder management
  • Integration of AI solutions with existing business processes
  • Mentoring of data scientists and ML engineers

My consulting approach combines academic rigor with practical industry experience, allowing me to bridge the gap between theoretical AI concepts and their commercial, real-world applications.
How did I transition from Academia to Industry?
My transition from academia to industry followed a strategic path driven by a strong desire to create more real-world impact.

Academic Foundation
I built a strong research foundation through:
  • Master's in Neuroscience at Oxford University
  • PhD at University College London
  • Wellcome Trust Postdoctoral Fellowship at Oxford
  • Published approximately 30 papers in his first 5 years of PhD

Motivation for Transition
Two key factors drove my transition to industry:
  • A desire to create more immediate impact for people, rather than focusing solely on basic research
  • Interest in applying my knowledge of biological neural networks to artificial neural networks for speech and language 

First Industry Role
My entry into industry began at Amazon Alexa AI in Seattle as a Research Scientist, where I:
  • Worked on end-to-end deep learning models for speech and natural language processing
  • Developed NLP classification models for detecting sensitive content in Alexa conversations

Transferable Skills
My academic background provided crucial skills for thriving in industry:
  • Hypothesis-driven thinking
  • First-principles approach
  • Experimental design expertise
  • Statistical analysis of large datasets
  • Communication and stakeholder collaboration

Additional Preparation
To succeed in industry, I also strategically upskilled in:
  • Software engineering 
  • Algorithms
  • Systems design
  • Python programming
  • Machine learning and deep learning model development

After Amazon, I progressed to leadership roles, including leading AI teams at Swiggy and serving as Head of AI at Docsumo, demonstrating a successful transition from academic researcher to AI industry leader.