Why I Coach All 4 AI Roles: My Career Across Academia, Big Tech, Startups & Consulting

I offer 1-on-1 AI career coaching for four distinct roles:

• Research Scientist
• Research Engineer
• AI Engineer
• Forward Deployed Engineer

People sometimes ask how one coach can credibly cover all four. The short answer:
I've done all four.

Over 17 years across academia, FAANG, startups, and independent consulting, my career has placed me inside each of these roles - not as an observer, but as a practitioner. That's what separates my coaching from generic career advice.

When I prepare candidates for an ML system design interview, I'm drawing on systems I've built. When I help you frame a research narrative, I'm drawing on papers I've published. When I coach you on client-facing AI consulting, I'm drawing on engagements I've delivered.

Here's how my career maps to each role I coach.

Research Scientist: A Decade of Original Research at Oxford and UCL
My career began in fundamental brain research. I earned my PhD in Neuroscience at University College London's Wellcome Trust Centre for Neuroimaging, studying how the brain processes time, rhythm, and auditory information. I then held a Sir Henry Wellcome Postdoctoral Fellowship at the University of Oxford -  one of the UK's most competitive early-career research awards.

Over roughly a decade in academia, I published 40+ peer-reviewed papers in top journals including the Journal of Neuroscience, Brain, and eLife accumulating 3,200+ citations. I presented at 50+ international conferences across the US, Canada, UK, Germany, Switzerland, and France, and received awards from the Royal Society, Wellcome Trust, and Max Planck Institute.

This work wasn't tangential to AI. My research in computational models of auditory cognition, neural timing mechanisms, and speech processing laid the direct foundation for my transition into deep learning and speech recognition.

What this means for my Research Scientist coaching
I understand the Research Scientist interview from the inside - the paper deep-dives where you're expected to critique methodology on the spot, the research taste questions probing where you'd push a field forward, and the expectation of rigorous first-principles thinking. I've been the researcher defending a novel hypothesis, and I've been the reviewer challenging one.

If you're preparing for a Research Scientist role at Google DeepMind, Meta, OpenAI, or Anthropic, I coach you from that lived experience.
→ Learn more about my Research Scientist coaching


Research Engineer: Applied Research at Amazon Scale & Startup Speed
At Amazon Alexa AI in Seattle, I operated as a Research Scientist whose work had to ship. I trained deep neural networks on thousands of hours of speech data and developed end-to-end speech recognition models serving millions of Alexa users worldwide.

I published at the Amazon Machine Learning Conference on offensive and sensitive content detection across multiple languages, and worked on privacy-preserving deep learning using homomorphic encryption and federated learning.

The tech stack was deep: Transformers, BERT, Seq2Seq, TensorFlow, MXNet, PyTorch, Fairseq, all deployed on AWS infrastructure at consumer scale.

At Swiggy, India's largest food delivery platform, I led the Conversational AI research team of ~10 applied scientists and engineers. I built applied NLP and Voice AI products: intent recognition, speech recognition for Hinglish customer service conversations, and voice sentiment analysis for call center automation. Every project started as a research question and ended as a deployed, revenue-impacting system.

What this means for my Research Engineer coaching
Research Engineering sits at the intersection of novel methods and production constraints. I've navigated that tension at FAANG scale and startup speed (shipping in weeks, not quarters).

Hiring managers for Research Engineer roles want to know: can you read a paper and turn it into something that works reliably in production? I coach candidates to demonstrate exactly that.
→ Learn more about my Research Engineer coaching


AI Engineer: Building and Scaling Production ML Systems
At Amazon Alexa AI, I built and deployed business-critical NLP classification models for content moderation - production systems with real SLAs, latency requirements, and millions of daily inferences.

At Swiggy, I built AI products end-to-end: chatbots, product classification, sentiment analysis - all deployed to a B2C platform processing millions of orders daily.

At Docsumo, an early-stage B2B Document AI startup, I served as Head of AI, leading a team of 25+ ML and Data Engineers. We built a Document AI platform using LLMs (GPT-3.5+), OCR, and Layout language models (Transformer architecture) for clients across banking, finance, and insurance.

I owned the full ML lifecycle: synthetic data pipelines, model training, table detection, information extraction, and production deployment.

What this means for my AI Engineer coaching
AI Engineer interviews test whether you can build, deploy, and scale - and whether you can communicate that ability under pressure. I've done all three at FAANG scale, at startup velocity, and in B2B enterprise contexts. I coach candidates on ML system design, MLOps thinking, and the communication patterns that separate L5 candidates from L6 ones.
→ Learn more about my AI Engineer coaching

​
Forward Deployed Engineer: Client-Facing Consulting Across Countries
As an independent AI consultant and advisor, I've worked directly with enterprises and startups across the US, UK, and India. My consulting work is the Forward Deployed Engineer role in its native form:

• Translating business goals into AI strategy - scoping what's technically feasible, commercially valuable, and deployable within real constraints
• Hiring, building, and mentoring AI teams from scratch - standing up capabilities where none existed
• Advising C-suite leaders on AI adoption - bridging the gap between executive ambition and engineering reality
• Delivering corporate AI training at the Indian School of Business and Adobe - teaching non-technical stakeholders to work effectively with AI teams
• Cross-functional collaboration with Engineering, Product, Analytics, and Business organisations to scope, build, and deploy GenAI solutions

What this means for my Forward Deployed Engineer coaching
FDE interviews are uniquely challenging because they test technical breadth, communication clarity, and business acumen simultaneously. Most coaches can help with one or two of those dimensions. I coach all three - because I've lived all three in client-facing consulting engagements where the stakes were real, the timelines were tight, and the audience wasn't always technical.
→ Learn more about my Forward Deployed Engineer coaching

The Full Picture: One Career, Four Roles

• When I coach a Research Scientist candidate, I draw on a decade of publishing in top-tier journals and defending research at international conferences.
• When I coach an AI Engineer, I draw on building production ML systems at Amazon scale and leading teams of 25+ engineers.
• When I coach a Research Engineer, I draw on the applied research I shipped at Alexa AI and Swiggy - work that started as papers and ended as products.
• When I coach a Forward Deployed Engineer, I draw on the client-facing consulting work where I translated ambiguous business problems into deployed AI solutions.

This isn't theoretical expertise. It's lived experience across every role I coach.

Ready to Work With a Coach Who's Been Where You're Going?

I've coached 100+ professionals into roles at Apple, Google, Meta, Amazon, Databricks, LinkedIn, Salesforce, and more - with typical salary increases of $100K–$200K.

Whether you're targeting a Research Scientist position at a top AI lab, a Research Engineer role at a FAANG company, an AI Engineer position at a scaling startup, or an FDE role at a company like Palantir - I can help because I've done the work myself.
→ Book a free 15 min discovery call

Not ready for a call yet?
Get my career guide for your target role:

• Research Scientist Interview Guide
• Research Engineer Interview Guide
• AI Engineer Interview Guide
• FDE Interview Guide​

FAQs

1 Can one career coach really help with all four AI roles?
Yes - if the coach has direct experience in each one.
Most career coaches specialise from the outside, studying role descriptions and interview formats. My coaching is different because I've actually worked as a Research Scientist (Oxford, UCL), Research Engineer (Amazon Alexa AI, Swiggy), AI Engineer (Amazon, Swiggy, Docsumo), and in client-facing AI consulting roles equivalent to a Forward Deployed Engineer. That breadth across academia, big tech, startups, and consulting means I coach from lived experience, not second-hand knowledge.

2 What makes your approach different from other AI career coaches?
Three things.
First, technical depth - I've built production ML systems, published in top journals, and led AI teams, so I can go as deep as you need on system design, LLMs, or research methodology.
Second, neuroscience-backed methods - my Oxford Postdoc and UCL PhD informs how I structure interview preparation, using evidence-based techniques for memory consolidation, stress management, and performance under pressure.
Third, breadth - I've worked across academia, FAANG, startups, and consulting across 4 different countries (US, UK, France, India), which means I understand the cultural and technical differences between these environments and can help you navigate them.

3 I'm a PhD considering industry roles. Can you help with that transition?
Absolutely.
I made the academia-to-industry transition myself, moving from a decade of research at Oxford and UCL to Amazon Alexa AI. Many of my 100+ successful placements have been PhDs making the same leap. I understand the unique challenges: reframing academic work for industry interviewers, choosing between Research Scientist and Research Engineer paths, navigating the cultural shift, and negotiating compensation. 
→ ​Book a strategy call and we can map out your best path.

4 Which role should I target? Research Scientist, Research Engineer, AI Engineer, FDE?
It depends on where your strengths and interests lie. Research Scientists drive original research and publish. Research Engineers take novel methods and make them work in production. AI Engineers build, deploy, and scale ML systems. Forward Deployed Engineers work directly with clients to solve business problems with AI. In a strategy call, I help you identify which role matches your background and career goals - and build a preparation plan specific to that path. 
→ Learn more about each role 

5 How do you use Neuroscience in your coaching?
My PhD research focused on how the brain processes information, forms memories, and remembers information across time. I apply these principles directly to interview preparation: spaced repetition for retaining system design patterns, interleaved practice for building flexible problem-solving skills, stress inoculation techniques for performing under interview pressure, and sleep optimisation for memory consolidation. It's not motivational fluff - it's peer-reviewed cognitive science applied to a high-stakes performance context.

6 What results do your clients typically see?
My clients have landed roles at Apple, Google, Meta, Amazon, Databricks, LinkedIn, Salesforce, Microsoft, and other top AI companies. Typical salary increases range from $100K to $200K. I've coached professionals from ML Engineer to Director level, across 20+ countries, with a strong track record in all four role types.