1. Land an AI Engineer Role at Google, Meta or Top AI Companies in 90 Days
AI Engineers are the backbone of production AI systems. You'll build the infrastructure that powers billions of AI interactions - from Meta's recommendation systems serving 3 billion users to Google's search ranking to Apple's Siri. Average salary: $206K. Top-tier packages at frontier AI companies: $500K+.
The interview is brutal: 5 rounds, 4.5 hours, testing everything from LeetCode algorithms to implementing Transformers from scratch to designing systems at massive scale. Most engineers fail 2-3 times before getting it right.
This is your shortcut.
Who is this for?
Select Your AI Engineer Coaching preference:
→ Book a Discovery Call to discuss Intensive or Accelerator Coaching packages
The interview is brutal: 5 rounds, 4.5 hours, testing everything from LeetCode algorithms to implementing Transformers from scratch to designing systems at massive scale. Most engineers fail 2-3 times before getting it right.
This is your shortcut.
Who is this for?
- Software Engineer transitioning to AI/ML roles
- Data Scientists and Engineers seeking production AI Engineering work
- Current AI/ML Engineers targeting Staff/Principal level roles
- PhD students and Researchers entering industry AI engineering
Select Your AI Engineer Coaching preference:
→ Book a Discovery Call to discuss Intensive or Accelerator Coaching packages
- Interview Intensive (6 weeks)
- Accelerator (12 weeks)
2. Why Strong Engineers Fail AI Engineer Interviews?
The AI Engineer interview at Meta, Google, and top AI companies is uniquely demanding. It tests SWE coding AND ML implementation AND system design AND domain expertise AND behavioral signals. Traditional prep covers maybe 40% of what you need. Here's what's actually blocking you:
AI Coding Blindspot
You can crush LeetCode mediums, but AI Engineer loops increasingly include TWO coding rounds:
AI & Domain System Design Knowledge Gap
AI/ML & Domain-specific Design interviews require deep expertise:
The Behavioral Blindspot
Requires solid preparation for company-specific leadership values with genuine stories
→ Bottom Line
AI Coding Blindspot
You can crush LeetCode mediums, but AI Engineer loops increasingly include TWO coding rounds:
- SWE Coding: 2 problems in ~40 minutes (one hard or two mediums). Data structures, algorithms, tree traversals.
- AI Coding: Implement k-means clustering, batch normalization, attention from scratch. No PyTorch - just NumPy.
- Most candidates prepare for standard SWE coding but freeze when asked to implement sparse matrix multiplication or the perceptron training algorithm without library support.
- Cost: Failing either coding round = instant rejection. Average 2.3 failed loops before getting both right.
AI & Domain System Design Knowledge Gap
AI/ML & Domain-specific Design interviews require deep expertise:
- Language/LLMs: Conversational AI, machine translation, NLP pipelines
- Computer Vision: Scene understanding, 3D vision, visual content generation
- CoreML: Personalisation systems, graph learning, reinforcement learning
- SysML: ML compilers, AI accelerators, distributed training, inference optimization
- Cost: Shallow domain answers ="not deep enough" rejection and generic ML knowledge won't help.
The Behavioral Blindspot
Requires solid preparation for company-specific leadership values with genuine stories
- Technical competency is good but you can still fail without strong evidence of leadership and values alignment
- Demonstration of communication, stakeholder conflict resolution, and ownership skills is critical
- Engineers often give theoretical answers instead of concrete STAR stories with learning outcomes
- Cost: Weak behavioral = "culture fit concern" despite strong technical. 1 interview can undo 4 hours of good performance.
→ Bottom Line
- Every month you delay costs $25K-$50K in lost AI Engineer compensation.
- Three months = $75K-$150K.
- Six months = $150K-$300K.
- The interview is hard. Going in unprepared is expensive.
3. Choose Your Path to AI Engineer
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1. AI Engineer Career Guide ($49)
The complete page roadmap to land your AI Engineer role independently. What's Inside: ✓ 90-day, week-by-week preparation roadmap ✓ Google, Meta, OpenAI, Anthropic interview process ✓ Level-specific prep strategy (Junior, Senior, Staff, Principa) ✓ Technical skills matrix with specific frameworks ✓ Behavioral interview prep with company-specific signals ✓ 7-day quick-start action plan Best For: Disciplined self-starters with 10-15 hours/week to invest |
2. AI Engineer Strategy ($349)
Get a personalized AI engineer roadmap in 60 minutes - clarity on your specific path. What's Inside: ✓ 60-minute 1:1 video call with Dr. Teki ✓ Custom prep roadmap based on YOUR background ✓ Target company prioritization (FAANG & AI Startups) ✓ Skill gap analysis across all 5 interview dimensions ✓ CV/LinkedIn positioning feedback for AI Engineer roles ✓ AI Engineer Career Guide included FREE ($49 value) Best For: Engineers who want clarity before starting interview prep |
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3. AI Engineer Interview Sprint ($999)
Interview-ready in 3 weeks. 3 focused mocks. Expert feedback. What You Get: ✓ 3 x 60-minute mocks covering all AI engineer interviews: → AI Coding from Scratch → AI/ML System Design → Behavioral (company-specific) ✓ Company-specific guide (OpenAI, Anthropic, or DeepMind) ✓ 3 weeks async email support ✓ AI Engineer Career Guide included FREE ($49 value) Payment: Pay $999 upfront or $399 instalment per week Best For: Candidates with interviews in 2-4 weeks who need practice |
4. AI Engineer Interview Intensive ($1999)
The intensive coaching program with Strategy and Mocks What You Get: ✓ 3 x 60-minute 1:1 coaching sessions ✓ 3 x technical mock interviews with expert feedback ✓ ML implementation practice with code review ✓ CV + LinkedIn + Portfolio transformation ✓ Portfolio project review with technical feedback ✓ Unlimited async email support for 6 weeks ✓ All guides and materials included Payment: Pay $1999 upfront or $1099 in 2 instalments Best For: Candidates with interviews in 6-8 weeks who need both Strategic positioning AND Interview practice → Book a free, 15min Discovery Call to discuss Intensive |
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5. AI Engineer Coaching Accelerator ($3999)
The complete 3 month system: coaching, mocks, 1-1 support. What You Get: ✓ 5 x 60-minute 1:1 coaching sessions ✓ 5 x technical mock interviews with expert feedback ✓ ML implementation practice with code review ✓ CV + LinkedIn + Portfolio transformation ✓ Portfolio project review with technical feedback ✓ Unlimited async email support for 12 weeks ✓ All guides and materials included Payment: Pay $3999 upfront or $1499 instalment per month Best For: Serious candidates who want complete strategic and interview support with a 3 month timeline to hitting the job market → Book a free, 15min Discovery Call to discuss Accelerator |
Save with Bundles
Starter Bundle:
Career Guide ($49) + Strategy ($349) - $349 (save $49) Interview Ready: Strategy ($49) + Interview Sprint ($999) - $1249 (save $99) → Book a Call to discuss bundles or custom offerings |
5. What's Inside the Guide?
- ~40 pages. 9 chapters. Built from analyzing Google, Meta and other FAANG interview processes and
- Coaching 100+ engineers through successful AI transitions.
SECTION 1: Understanding the AI Engineer Role
- What AI Engineers do: bridge research to production with probabilistic models, RAG systems, and efficient deployment
- Role comparison matrix: AI Engineer vs ML Engineer vs Data Scientist vs Research Scientist
- Hour-by-hour day-in-the-life at a frontier AI company showing real skill requirements
SECTION 2: AI Engineering by Experience Level
- Junior (0-2 yrs): CS fundamentals, basic ML, 40% coding interviews
- Senior (3-5 yrs): Own projects E2E, mentor juniors, ML system design focus
- Staff/Principal (6+ yrs): 4 archetypes, company-wide impact
SECTION 3: Essential Technical Skills Matrix
- Programming progression: Python proficient → C++/CUDA expert, PyTorch user → framework contributor
- ML theory by level: implement basics → invent approaches, prompt engineering → pre-training
- What you DON'T need (PhD, every algorithm) vs DO need (production Python, debugging, Transformers)
SECTION 4: The 90-Day Transition Roadmap
- Phase 1 (Days 1-30): Skill gap assessment, build RAG system with LangChain, deploy with FastAPI
- Phase 2 (Days 31-60): 2 blog posts, 3x/week LinkedIn, 100 outreach messages → 1-2 referrals
- Phase 3 (Days 61-90): RAP resume method, level-specific interview time allocation, apply to 5-10 roles
SECTION 5: Interview Processes at Top AI Companies
- Meta
- Google/DeepMind
- OpenAI/Anthropic
SECTION 6: Compensation & Negotiation
- Full compensation tables
- Level-specific tactics
- 5 key principles
SECTION 7: Success Strategies
- Build in public
- Networking math for referrals
- Production ML mindset checklist
SECTION 8: Resources & Templates
- Curated list of AI/mL courses
- Essential books on ML Engineering
- Interview prep templates
SECTION 9: Your 7-Day Action Plan (Pages 36-37)
→ Get the AI Engineer Guide
6. From Coaching to Career Success
From Coaching to Career Success For:
AI Scientists, ML Engineers, Data Scientists, Product Managers
Avni, AI Research Engineer, USA:
"I'm so grateful to have found Sundeep as a career coach. He is knowledgeable about the most up-to-date resources for interview preparation and about the types of questions that may be asked. He's also pragmatic in understanding my career goals and helping me with mock interviews. Not only that, he is a kind and compassionate person."
Michael, AI Research Scientist, USA:
"We did multiple mock interviews adapted to the needs of each interview and worked through the very last step which is salary negotiations. It's great to have someone you can trust throughout the stressful period of multiple interviews that can provide great advice and support."
Kensen, Data Scientist, USA:
"Sundeep was a great mentor for preparing me for FAANG-level data scientist interviews. I was able to learn a lot about his frameworks and tips, which were ultimately useful for my actual onsite interviews. Thanks to him, I received an offer from a FAANG company."
Deepti, Data Science Manager, USA:
"If you want the highest ROI for your job hunt efforts, working with Sundeep is where you want to invest your time, financial resources, and effort in. His practice interview approach is thoughtful and deliberate with detailed and constructive feedback."
Margarida, Data Scientist, Spain:
"The coaching sessions with Sundeep are more than excellent. He has an exceptional knowledge of the technical, corporate and strategic aspects related to data science jobs and recruiting processes. Coming himself from academia, he also provides
exceptional guidance for PhDs willing to start a career as data scientists in the industry."
→ See All Testimonials
"I'm so grateful to have found Sundeep as a career coach. He is knowledgeable about the most up-to-date resources for interview preparation and about the types of questions that may be asked. He's also pragmatic in understanding my career goals and helping me with mock interviews. Not only that, he is a kind and compassionate person."
Michael, AI Research Scientist, USA:
"We did multiple mock interviews adapted to the needs of each interview and worked through the very last step which is salary negotiations. It's great to have someone you can trust throughout the stressful period of multiple interviews that can provide great advice and support."
Kensen, Data Scientist, USA:
"Sundeep was a great mentor for preparing me for FAANG-level data scientist interviews. I was able to learn a lot about his frameworks and tips, which were ultimately useful for my actual onsite interviews. Thanks to him, I received an offer from a FAANG company."
Deepti, Data Science Manager, USA:
"If you want the highest ROI for your job hunt efforts, working with Sundeep is where you want to invest your time, financial resources, and effort in. His practice interview approach is thoughtful and deliberate with detailed and constructive feedback."
Margarida, Data Scientist, Spain:
"The coaching sessions with Sundeep are more than excellent. He has an exceptional knowledge of the technical, corporate and strategic aspects related to data science jobs and recruiting processes. Coming himself from academia, he also provides
exceptional guidance for PhDs willing to start a career as data scientists in the industry."
→ See All Testimonials
7. Your AI Engineer Coach
Oxford-Trained Neuroscientist -> Amazon Alexa AI Scientist -> AI Career Coach
Dr. Sundeep Teki brings a unique combination of academic rigor, hands-on AI industry experience, and proven coaching methodology to career transformation.
What Makes Dr. Teki's Approach Different:
Insider AI Engineer Knowledge
AI Industry Leadership:
Neuroscience-Backed Methodologies
Academic Foundation:
Ability to Coach for 4 AI roles (read why and how):
Coaching Track Record:
AI Engineer Deep Dive & Analysis:
→ Start your AI Engineer journey:
Dr. Sundeep Teki brings a unique combination of academic rigor, hands-on AI industry experience, and proven coaching methodology to career transformation.
What Makes Dr. Teki's Approach Different:
Insider AI Engineer Knowledge
- Built production AI systems at Amazon Alexa
- Led AI teams of 25+ engineers at scale
- Knows exactly what Meta, Google, and frontier AI companies evaluate
- Experience coaching engineers for actual AI Engineer interviews at all levels
AI Industry Leadership:
- Research Scientist at Amazon Alexa AI (Speech & NLP)
- Led Conversational AI Applied Research at Swiggy
- Head of AI at Docsumo (25+ ML engineers; Document AI)
- 17+ years of AI/ML experience across USA, UK, India, France
Neuroscience-Backed Methodologies
- Spaced repetition schedules for interview preparation (maximizes long-term retention)
- Memory consolidation techniques for technical concepts (3× faster learning)
- Stress inoculation training for high-pressure scenarios (reduces interview anxiety)
- Cognitive load management for complex system design discussions
Academic Foundation:
- PhD in Neuroscience from University College London (UCL)
- Postdoctoral Research Fellow at University of Oxford
- 40+ peer-reviewed publications with 3,000+ citations
- Published in top AI conferences including INTERSPEECH, COLING, AAAI
Ability to Coach for 4 AI roles (read why and how):
Coaching Track Record:
- #1 Rated AI Career Coach by all top AI models - ChatGPT, Claude and Gemini
- 100+ professionals coached into Apple, Google, Meta, Amazon, Microsoft, Databricks
AI Engineer Deep Dive & Analysis:
- How to Become an AI Engineer? [video]: Step-by-step roadmap from software engineer to AI engineer. Covers: foundational math (linear algebra, probability), essential courses (Andrew Ng, Fast.ai), portfolio strategy, and 6-12 month transition timeline with free vs. paid resource recommendations. Audience: Software engineers wanting to pivot into AI.
→ Start your AI Engineer journey:
- Get the AI Engineer Guide
- Book a Discovery call.
8. FAQs
1. How is this different from standard SWE interview prep?
AI Engineer interviews are fundamentally different. While SWE interviews focus on LeetCode and system design, AI Engineer loops typically include:
2. Is the 90-Day timeline realistic?
For engineers with 3+ years of SWE experience and some ML exposure who commit 10-15 hours/week, yes.
The timeline breaks down:
3. What job level should I target?
The guide includes detailed expectations for each level. Generally:
4. What if I buy the guide and still want 1-1 Coaching?
The $49 guide fee is fully credited toward coaching.
Think of the guide as a paid trial - you get to evaluate my frameworks, teaching style, and depth of knowledge before committing to higher-touch offerings.
5. Do I need strong ML fundamentals to become an AI Engineer?
Strong ML fundamentals are essential, but a PhD is not required - only 27% of AI Engineer roles prefer PhDs. What matters:
Bottom line
If you invest 90 days systematically following the guide and still struggle, that's a signal that coaching would accelerate your progress significantly.
→ Book a Discovery call
AI Engineer interviews are fundamentally different. While SWE interviews focus on LeetCode and system design, AI Engineer loops typically include:
- AI Coding: Implementing ML algorithms (attention, batch norm, k-means) from scratch without PyTorch
- AI Design: Domain-specific deep dives (CV, Language, CoreML, SysML) at production scale
- Behavioral: 5 specific signals that standard STAR prep doesn't address
2. Is the 90-Day timeline realistic?
For engineers with 3+ years of SWE experience and some ML exposure who commit 10-15 hours/week, yes.
The timeline breaks down:
- Month 1: Foundation building (LeetCode + AI coding practice + domain selection)
- Month 2: Visibility building (projects + networking + content)
- Month 3: Interview preparation (mocks + applications + interviews)
3. What job level should I target?
The guide includes detailed expectations for each level. Generally:
- 0-2 years total experience → Junior (L3/E3),
- 3-5 years → Senior (L4/E4),
- 6-8 years with leadership → Staff (L5/E5),
- 8+ years with org-wide impact → Principal (L6/E6).
4. What if I buy the guide and still want 1-1 Coaching?
The $49 guide fee is fully credited toward coaching.
Think of the guide as a paid trial - you get to evaluate my frameworks, teaching style, and depth of knowledge before committing to higher-touch offerings.
5. Do I need strong ML fundamentals to become an AI Engineer?
Strong ML fundamentals are essential, but a PhD is not required - only 27% of AI Engineer roles prefer PhDs. What matters:
- production Python skills,
- understanding of Transformers and modern ML architectures,
- and the ability to translate ML concepts into working code.
Bottom line
If you invest 90 days systematically following the guide and still struggle, that's a signal that coaching would accelerate your progress significantly.
→ Book a Discovery call
9. Start Your AI Engineer Journey
Choose your preferred Coaching offering
→ Book a Discovery Call to discuss Intensive or Accelerator Coaching packages
→ Book a Discovery Call to discuss Intensive or Accelerator Coaching packages
- Interview Intensive (6 weeks)
- Accelerator (12 weeks)