Abhay - Python tutor - Bengaluru
1st lesson free
Abhay - Python tutor - Bengaluru

Abhay's profile and contact details have been verified by our team

Abhay

  • Rate R262
  • Response 6h
  • Students

    Number of students Abhay has accompanied since joining Superprof

    44

    Number of students Abhay has accompanied since joining Superprof

Abhay - Python tutor - Bengaluru
  • 5 (5 reviews)

R262/h

1st lesson free

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1st lesson free

1st lesson free

  • Python
  • Artificial Intelligence

Teaching LLMs, RAG, LangChain, FastAPI | Microsoft Certified AI Engineer | 2 Yrs Exp | AI Engineer at Cisco

  • Python
  • Artificial Intelligence

Lesson location

Recommended

Abhay is a respected tutor in our community. He is highly recommended for his commitment and the quality of his lessons. A trusted partner on your learning journey.

About Abhay

I Build Production AI Systems - Not Just Jupyter Notebook Demos

Hi, I'm Abhay - an AI Engineer with 2+ years of hands-on experience building enterprise-scale AI systems that deliver real business impact:

Production based platforms → ≈₹9 Cr annual savings
ML pipeline processing 20,000+ documents monthly → 94% accuracy
LLM-powered semantic search → 12,000+ queries/month across 4 business units
RAG systems with hybrid retrieval (vector + keyword) in production

Tech Stack I Use Daily:
• LLMs: GPT-4, Claude, Gemini, open-source (Llama, Mistral)
• Frameworks: LangChain, LangGraph, LlamaIndex
• Vector DBs: Redis Stack, Pinecone, ChromaDB, Elasticsearch
• Backend: FastAPI, Redis, PostgreSQL, MongoDB
• Deployment: Docker, Kubernetes, Azure, GCP
• MLOps: MLflow, Weights & Biases, CI/CD pipelines

What Makes Me Different:

Guest Speaker at National Faculty Development Program (900+ professors & researchers)
Published AI/ML researcher (ML security, Sanskrit NLP)
Amazon ML Challenge 2023: AIR 94 out of 26,008 participants
Mentored 9 engineers → 3 converted to full-time AI roles
Conducted 100+ technical interviews for AI/ML positions
Presented work projects to Dept. of Science & Technology (Govt. of India) & Mahindra Group CTO Mohit Kapoor & Mahindra AI CEO Bhuwan Lodha

Who Should Learn From Me:

→ Professionals building AI/ML portfolios for FAANG/startup roles
→ Developers wanting to add LLM/GenAI skills (hottest market right now)
→ Engineers transitioning from traditional ML to production GenAI
→ Founders evaluating AI implementation for their products
→ Students targeting top-tier placements with cutting-edge skills

I teach what YouTube tutorials skip: deployment, scalability, cost optimization, production debugging.

B.Tech CS (8.7 CGPA) | Microsoft Azure Data Scientist | Azure AI Fundamentals | Google & IBM Data Science Certified | LeetCode Knight (Top 4.77%) | 2 years work experience building application with a cost impact of ≈₹9 Cr for an MNC.

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About the lesson

  • All levels
  • English

Languages in which the lesson is available :

English

Production-Ready AI/ML Training - From Fundamentals to Deployment

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TRACK 1: ML FUNDAMENTALS → JOB-READY
(12-16 weeks | Best for beginners/intermediates)

Phase 1: Python + Math for ML (3-4 weeks)
• NumPy, Pandas - data manipulation at scale
• Linear algebra, probability, statistics (applied, not theoretical)
• Matplotlib, Seaborn, Plotly - visualization
• Git/GitHub for portfolio building

Phase 2: Classical Machine Learning (4-5 weeks)
• Supervised: Linear/Logistic Regression, Decision Trees, Random Forest, XGBoost
• Unsupervised: K-Means, DBSCAN, PCA, t-SNE
• Model evaluation: Cross-validation, metrics (precision, recall, F1, AUC-ROC)
• Feature engineering & selection
• Scikit-learn mastery
• Project: End-to-end ML pipeline with real dataset

Phase 3: Deep Learning (4-5 weeks)
• Neural networks from scratch (understand backprop deeply)
• TensorFlow/Keras & PyTorch
• CNNs: Image classification, object detection, transfer learning
• RNNs/LSTMs: Time series, sequence modeling
• Transformers introduction
• Project: Computer vision or NLP model

Phase 4: Production & Deployment (3-4 weeks)
• FastAPI for model serving (REST APIs)
• Docker containerization
• Cloud deployment (Azure ML, GCP Vertex AI)
• Model monitoring & versioning
• Basic MLOps (CI/CD for ML)
• Capstone: Deploy ML app accessible via API

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TRACK 2: GENERATIVE AI & LLMs
(8-12 weeks | Assumes Python + basic ML knowledge)

**This is the HOTTEST skill in 2026-27. Companies are desperately hiring.**

Module 1: LLM Fundamentals (2 weeks)
• Transformer architecture deep-dive (attention is all you need)
• Tokenization, embeddings, context windows
• OpenAI API, Anthropic Claude, Google Gemini
• Hugging Face ecosystem (models, datasets, spaces)
• Prompt engineering: zero-shot, few-shot, chain-of-thought, ReAct
• Cost optimization (model selection, caching, batching)

Module 2: RAG - Retrieval Augmented Generation (3 weeks)
• Why RAG > fine-tuning for most use cases
• Document processing: PDF, DOCX, HTML parsing
• Chunking strategies (recursive, semantic, sentence-window)
• Embeddings: OpenAI, Cohere, open-source (Sentence-BERT)
• Vector databases: Pinecone, ChromaDB, Weaviate, FAISS
• Hybrid search: Vector + BM25/Elasticsearch
• Reranking strategies
• Evaluation: RAGAS, custom metrics
• Project: Build RAG chatbot for custom knowledge base

Module 3: LangChain & Agentic AI (3 weeks)
• LangChain components: Chains, Agents, Tools, Memory
• LangGraph for stateful multi-step workflows
• Function calling & tool use
• Multi-agent systems
• Autonomous agents (plan-and-execute patterns)
• Project: AI agent that can browse web, query databases, generate reports

Module 4: Production LLM Systems (2-4 weeks)
• FastAPI microservices architecture
• Redis/PostgreSQL for conversation memory
• Rate limiting, quota management
• Authentication (OAuth, API keys)
• Streaming responses (SSE)
• Monitoring: Token usage, latency, cost tracking
• Guardrails & safety (prompt injection prevention)
• Capstone: Production-grade AI assistant with RAG + agents

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TRACK 3: AI/ML INTERVIEW PREP
(6-8 weeks intensive)

Week 1-2: ML Theory Deep-Dive
• 100+ most-asked ML interview questions
• Math refresher: Probability, linear algebra, calculus
• Bias-variance, regularization, optimization
• Model comparison frameworks

Week 3-4: Coding for ML Roles
• Python/Pandas/NumPy challenges
• SQL for data analysis
• LeetCode patterns (arrays, trees, DP basics)
• Take-home assignment walkthroughs

Week 5-6: ML System Design
• End-to-end ML system design framework
• Case studies: Recommendation, search ranking, fraud detection, feed ranking
• Scalability, latency, cost trade-offs
• A/B testing & experimentation platforms

Week 7-8: Mock Interviews
• Live technical rounds (I've conducted 100+ real ones)
• ML debugging scenarios
• Behavioral questions (STAR method)
• Salary negotiation tactics

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

WHAT'S INCLUDED:

• Live walkthroughs of my production systems (≈ ₹9Cr impact projects)
• Curated resource library (200+ papers, tutorials, GitHub repos)
• Resume + LinkedIn optimization for AI roles
• WhatsApp support (response within 24 hrs)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

TEACHING PHILOSOPHY:

• 80% hands-on coding, 20% theory
• Real messy datasets - not toy examples
• Build → Break → Debug → Understand
• Every project is portfolio-worthy & interview-ready

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Languages: English, Hindi

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Rates

Rate

  • R262

Package rates

  • 5h: R1312
  • 10h: R2624

travel

  • + R299

free lesson

The free first lesson with Abhay allows you to get to know the tutor and discuss your needs and expectations.

  • 30min

online

  • R262/h

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