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Vector 2.0 · Starts 21 Nov 2026

Become a Gen-AI Developer in 3 months

Even with Zero Gen-AI Experience.

Build, deploy and scale five real AI products. Rehearse five mock interviews. Walk into your next interview ready — all you need today is Python, Docker & Git.

Enroll — ₹20,000
Free interview PDF
  • Starts 21 Nov 2026
  • ·
  • Vector 2.0 · 150 engineers per batch
  • ·
  • Last date to register: 11 Nov 2026
What's new · Vector 2.0

Vector 1.0 is complete. 2.0 levels up.

The first cohort is done. Vector 2.0 takes everything we learned and adds what companies now hire for — deployment, scale, evaluation, observability and security.

8 sprints

From FastAPI to LLMOps

A brand-new Sprint 0 makes you genuinely good at FastAPI before any AI. Then LLMs & LangChain, RAG, LangGraph, agents, cloud AI and scale — step by step.

Agents

MCP + A2A, secured

Multi-agent systems in LangGraph, CrewAI and Microsoft Agent Framework — connected with MCP and Agent2Agent, guarded and red-teamed with promptfoo.

New

Decision models (System One)

Typed, calibrated decisions for routing, scoring and verification in milliseconds — built into three of the five projects, with an LLM fallback path.

Cloud AI

Fine-tuning, AWS Bedrock & Azure AI

Fine-tune with LoRA/QLoRA, then build on both enterprise platforms — Bedrock Knowledge Bases, Agents & Guardrails and Azure OpenAI, AI Search & Foundry.

Flagship

Scale to millions of users

TokenGrid — vLLM serving, a LiteLLM gateway, rate limiting, caching, circuit breakers and Kubernetes — designed for 10M requests a day and load-tested.

Career

Demo Day with hiring partners

Present your platform live to peers, alumni and invited hiring partners — then a full senior mock loop, resume rewrite and LinkedIn audit.

Early-bird fee: ₹20,000 ₹25,000 till 30 Oct 2026

What you walk away with

One cohort, four hard outcomes.

5
Projects
Production-grade, deployed, evaluated and load-tested
5
Interviews
1:1 mocks with detailed written feedback
8
Sprints
FastAPI → LLMs → RAG → agents → cloud → LLMOps
10
M req/day
What your flagship platform is designed to handle
Swipe for more
Why this is different

What Sets Vector 2.0 Apart From Other Gen-AI Courses

Vector 2.0

Vector 2.0 — Production Gen-AI Cohort

  • Live, project-led cohort with sprint milestones
  • 5 production systems — containerised, tested, evaluated, deployed
  • Stack: FastAPI · LangGraph · MCP · A2A · Bedrock · Azure AI · vLLM
  • Scale to millions: gateways, caching, circuit breakers, load tests
  • 5 one-on-one mock interviews with written feedback
  • Demo Day with invited hiring partners
  • 150 engineers per batch — every capstone reviewed 1:1
Other courses

Generic Gen-AI courses

  • Pre-recorded videos, no deadlines
  • Toy notebooks that never see production
  • LangChain basics and RAG pseudocode only
  • No deployment, no scaling, no load testing
  • No mock interviews, generic worksheets
  • Certificate at the end — no portfolio to defend
  • Thousands of students, zero personal review
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How it works

Three things. That change everything.

A practitioner-led path with real builds and real interviews — designed so by Demo Day your GitHub, resume and confidence are all interview-ready.

A developer teaches you

Not a course creator. A Sr. Gen-AI Developer who ships these systems in production — and trained the Vector 1.0 cohort.

  • Live class
  • Real practitioner
  • Code reviews

You build and scale five real systems

Not toy demos. Five production Gen-AI systems on your GitHub — including a platform designed for millions of requests a day.

  • Deployed
  • Load-tested
  • Defendable

You rehearse five interviews

Real questions. Real feedback. Then Demo Day in front of hiring partners. Your actual interview becomes the sixth one, not the first.

  • 1:1 mocks
  • Written feedback
  • Demo Day
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Build it. Defend it. Get hired. Eight sprints.
The stack

Tools that show up in real 2026 Gen-AI job descriptions.

All taught hands-on with production patterns — never as toy notebooks.

20 tools · 4 layers
FastAPI logo
FastAPI
Python API
LangChain logo
LangChain
Agent framework
LangGraph
Stateful agents
Pydantic logo
Pydantic
Data validation
OpenAI logo
OpenAI
GPT models
Anthropic Claude logo
Anthropic
Claude models
MCP logo
MCP
Tool protocol
A2A
Agent protocol
TypeSafe AI Jev decision model logo
Jev
Decision model
HuggingFace logo
HuggingFace
Open models
Pinecone vector database logo
Pinecone
Vector DB
Qdrant vector database logo
Qdrant
Vector DB
PostgreSQL logo
Postgres
+ pgvector
RAGAS
RAG evals
AWS Bedrock logo
AWS Bedrock
Inference cloud
Azure AI logo
Azure AI
Enterprise AI
vLLM
Model serving
Kubernetes logo
Kubernetes
Autoscaling
Docker logo
Docker
Containers
Redis logo
Redis
Cache + limits
+ production tooling you'll use
LangSmith
Langfuse
LiteLLM
CrewAI
Microsoft Agent Framework
Docling
DeepEval
Guardrails AI
promptfoo
Presidio
Ollama
OpenTelemetry
Prometheus
Grafana
k6 load tests
GitHub Actions
ECS Fargate
Azure Container Apps
Capabilities

Six capabilities that get you hired.

01 / 06

Build reliable LLM features on FastAPI

  • Production FastAPI — validation, Postgres, auth, testing, Docker
  • LangChain — structured outputs, tools, agents, middleware, LCEL
  • Function calling with guarantees, fallbacks and cost control
02 / 06

Ship production RAG at scale

  • Hybrid search (BM25 + vectors) with reranking and citations
  • Multi-tenant, permission-aware retrieval that never leaks data
  • RAGAS & DeepEval quality gates that block regressions in CI
03 / 06

Engineer durable agentic workflows

  • LangGraph state, durability and human-in-the-loop
  • Queue-driven workers that never lose an item
  • Decision models for fast, calibrated routing and scoring
04 / 06

Design secure multi-agent systems

  • MCP servers (Streamable HTTP + OAuth) and A2A protocol
  • LangGraph, CrewAI and Microsoft Agent Framework
  • OWASP LLM & Agentic Top 10, guardrails and red-teaming
05 / 06

Fine-tune and build on AWS Bedrock & Azure AI

  • LoRA / QLoRA fine-tuning — and when to fine-tune vs use RAG
  • Bedrock Knowledge Bases, Agents, Guardrails and customisation
  • Azure OpenAI, AI Search, Foundry Agent Service, Content Safety
06 / 06

Deploy and scale LLMs for millions of users

  • vLLM serving, LiteLLM gateway, Kubernetes and safe releases
  • Rate limiting, caching, queues, circuit breakers and autoscaling
  • Observability, cost tracking and load tests that prove it
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Curriculum

Eight sprints. Five projects. One portfolio you can defend.

Who this sprint is for

You know Python but have never built an API, or you've built simple APIs but never thought about validation, authentication, databases, testing and project structure together.

You'll learn
  • — HTTP and REST fundamentals — methods, status codes, headers, request and response bodies
  • — FastAPI essentials — routing, path and query parameters, request bodies, response models and automatic API docs
  • — Pydantic for validation — models, field validation, nested data and settings management
  • — Async Python in practice — async/await, when async helps, and why one blocking call slows every user
  • — Dependency injection — sharing database sessions, configuration and authentication cleanly across routes
  • — Databases — Postgres with SQLAlchemy, schema design basics and migrations with Alembic
  • — Authentication and security — API keys, JWT login, password hashing and protected routes
  • — Background tasks and file uploads — safe uploads and work that runs after the response
  • — Error handling, middleware and logging — consistent errors, request IDs and structured logs
  • — Project structure and Docker — clean layout, env-based config, Postgres + Redis via docker compose
  • — AI-assisted development with Claude Code, Cursor and GitHub Copilot — plan first, test always, review every change
By the end you can
  • Build and structure a production-ready FastAPI application from scratch
  • Validate data, connect to Postgres, and secure your endpoints
  • Write tests and run your app with Docker
  • Use an AI coding assistant without shipping code you don't understand
Project shipped

Launchpad — Production FastAPI Starter Kit

A reusable, tested, Dockerised FastAPI template with authentication, database, migrations, logging and CI already wired in. Every project in the cohort starts from Launchpad.

Who this sprint is for

You've never called an LLM API or used LangChain, or you've used them but never treated reliability, cost and latency as engineering problems.

You'll learn
  • — How LLMs work — tokens, embeddings, attention, context windows and sampling, at the depth interviewers expect
  • — LLM providers — OpenAI, Anthropic Claude and local open-source models with Ollama; when to choose which
  • — The model landscape — generative, reasoning and decision models (System One models such as TypeSafe AI's Jev)
  • — Prompt engineering for production — instructions, few-shot examples, delimiters and consistent behaviour
  • — LangChain chat models, messages and content blocks with init_chat_model
  • — Prompt templates — reusable, versionable prompts with variables and few-shot examples
  • — Structured outputs — guaranteed Pydantic objects with with_structured_output, validation and retries
  • — Tools and tool calling — @tool, binding tools to models and handling tool errors
  • — Agents with create_agent — your first tool-using agent and the loop behind it
  • — Middleware — PII masking, summarisation, call limits and model fallbacks without rewriting the agent
  • — LCEL and runnables — composing with |, parallel steps, .with_retry() and .with_fallbacks()
  • — Streaming, batching, conversation memory, caching and rate limiting
  • — Vision inputs — extracting reliable data from images and scanned documents
  • — Tracing with LangSmith and streaming responses through FastAPI with Server-Sent Events
  • — Prompt injection basics and the first line of defence
By the end you can
  • Explain how LLMs work the way a senior interviewer wants to hear it
  • Use LangChain's core features — models, prompts, structured output, tools, agents, middleware and LCEL — with confidence
  • Build LLM features that return reliable structured data every time
  • Reason about cost, latency and reliability as first-class engineering concerns
Project shipped

LedgerLens — GST Invoice Intelligence API

An async FastAPI + LangChain service that reads invoices and receipts (PDFs and photos), extracts GST-ready structured data, validates it with business rules, and routes uncertain results to a human review queue.

Who this sprint is for

You've never built RAG, or you've built a notebook demo but can't explain why your chunking, retrieval or evaluation choices are right.

You'll learn
  • — RAG from first principles — embeddings, similarity search and why retrieval makes LLMs accurate and current
  • — LangChain for RAG — loaders, text splitters, embedding models, vector stores and retrievers
  • — Vector databases — Pinecone, pgvector and Qdrant compared; indexes, metadata filters, managed vs self-hosted
  • — Ingestion pipelines — parsing PDFs, tables and scans with Docling; background workers; updates and deletions
  • — Chunking strategies — why fixed-size fails, structure-aware chunking and retrieval-boosting metadata
  • — Embedding model selection — quality, cost, dimensions and multilingual support for Indian languages
  • — Hybrid search — BM25 plus vectors with reciprocal rank fusion for circular numbers, section IDs and product codes
  • — Reranking with cross-encoders inside a strict latency budget
  • — Query understanding — rewriting, multi-query expansion and conversational follow-ups
  • — Grounded answers — citations, source attribution and saying "I don't know"
  • — Multi-tenant and secure RAG — tenant isolation and document permissions enforced at query time
  • — Evaluation — RAGAS and DeepEval metrics, trusted test datasets and CI quality gates
  • — Scaling and cost — answer and embedding caching, latency budgets and scaling vector databases
By the end you can
  • Build a RAG pipeline that survives real user questions — not just demo questions
  • Defend every chunking, retrieval and reranking decision
  • Build secure, multi-tenant retrieval that never leaks data between customers
  • Measure RAG quality and block regressions before they ship
Project shipped

RegRadar — Compliance Copilot for Indian Fintech

A multi-tenant RAG service that answers compliance questions from RBI and SEBI circulars plus each company's private policies, with precise citations, change tracking, and quality gates in CI.

Who this sprint is for

You've never built a multi-step AI workflow, or you have one that breaks when a step fails, a server restarts, or traffic spikes.

You'll learn
  • — Workflows vs agents — when a predictable workflow is right, and why most production systems are mostly workflow
  • — LangGraph fundamentals — state, nodes, edges, conditional routing, cycles, parallel steps and subgraphs
  • — State design — typed state, reducers, and what belongs in state vs your database
  • — Durable execution — Postgres checkpointer so workflows resume exactly where they stopped after a crash
  • — Human-in-the-loop — approve / edit / reject through your API and resume cleanly
  • — Decision models in workflows — TypeSafe AI's Jev for classify, route and score steps with calibrated probabilities in milliseconds
  • — Confidence thresholds so only uncertain cases reach a human or a bigger model
  • — Queues and workers for high-volume processing with safe retries
  • — Handling sensitive data — detecting and masking personal and health information with Presidio
By the end you can
  • Design LangGraph workflows with typed state, branching and parallel steps
  • Build workflows that survive crashes and resume from saved state
  • Process thousands of items reliably with queues and workers
  • Combine LLM judgement with deterministic rules for trustworthy results
Project shipped

ClaimSense — Health Insurance Claims Automation Engine

A durable, queue-driven LangGraph workflow that reads claim documents, checks them against policy rules, flags anomalies, calculates the payable amount, and brings in a human adjuster only when needed.

Who this sprint is for

You've never built an agent, or you've built one but couldn't design a secure multi-agent system from scratch in an interview.

You'll learn
  • — Agent fundamentals — the reason–act loop, tools, memory and goals
  • — Agent design patterns — ReAct, planner–executor, reflection, supervisor and handoffs
  • — Tool design — single-responsibility tools, clear schemas, idempotency and actionable errors
  • — Loop control — step limits, cost ceilings, timeouts and graceful failure
  • — Model Context Protocol (MCP) — tools, resources and prompts; stdio and Streamable HTTP; OAuth; MCP Inspector
  • — MCP security — scoped permissions, tool poisoning and safe token handling
  • — Agent2Agent (A2A) protocol — Agent Cards, tasks and streaming; MCP vs A2A
  • — Multi-agent orchestration — supervisor and hierarchical patterns in LangGraph; shared vs isolated state
  • — Multi-framework development — the same agent in CrewAI and Microsoft Agent Framework, and "why this framework?"
  • — Guardrails and approvals — Guardrails AI or NeMo Guardrails, least-privilege tools, mandatory human approval
  • — Red-teaming your own agent with promptfoo
  • — A decision layer for agents — fast, bounded choices (which tool, retry or stop, escalate) in front of the LLM
  • — Serving agents to many users — stateless servers, session storage, concurrency and streaming
By the end you can
  • Design and defend single- and multi-agent architectures
  • Build secure, spec-compliant MCP servers and connect agents with A2A
  • Build agents in LangGraph, CrewAI and Microsoft Agent Framework, with a decision model handling fast, bounded choices
  • Threat-model, guard and red-team an agent system before it reaches users
Project shipped

TripPilot — Multi-Agent AI Travel Concierge

Tell it "Goa, 4 days, ₹40k, two people" — a team of AI agents searches flights, trains and hotels through MCP servers, checks your budget with a partner agent over A2A, builds a day-by-day itinerary, and books only after you approve.

Who this sprint is for

You can't yet answer "why fine-tune instead of using RAG?" from experience, or you've never built on a managed cloud AI platform like Bedrock or Azure AI.

You'll learn
  • — When to fine-tune — prompting vs RAG vs fine-tuning, with cost and time trade-offs
  • — How fine-tuning works — full vs parameter-efficient; LoRA and QLoRA
  • — Preparing training data — cleaning, formatting, synthetic data and train/test splits
  • — Hands-on fine-tuning with Hugging Face, PEFT, TRL and Unsloth on a real task from your projects
  • — Evaluating and shipping your model — vs a larger model and RAG; merging adapters and pushing to the Hub
  • — Three ways to make the same decision — prompted LLM vs fine-tuned model vs decision model, compared with data
  • — AWS Bedrock — model access, IAM, the Converse API and using Bedrock from LangChain
  • — Bedrock Knowledge Bases, Agents, Guardrails and model customisation
  • — Bedrock cost and capacity — on-demand vs provisioned throughput, batch inference and prompt caching
  • — Azure AI — Microsoft Foundry, model catalog and Azure OpenAI deployments from LangChain
  • — Secure Azure access — managed identities, Key Vault, RBAC and private endpoints
  • — Azure AI Search for RAG, Foundry Agent Service, Content Safety & Prompt Shields, Document Intelligence
  • — Fine-tuning on Azure and cost & capacity — standard vs provisioned deployments, batch API and quotas
  • — Choosing the right platform — Bedrock vs Azure AI vs direct APIs vs self-hosting, and which companies use what
By the end you can
  • Decide between prompting, RAG and fine-tuning — and defend the decision with data
  • Fine-tune an open-source model with LoRA/QLoRA and evaluate it honestly
  • Build Gen-AI features on AWS Bedrock using models, Knowledge Bases, Agents and Guardrails
  • Build Gen-AI features on Azure AI using Azure OpenAI, AI Search, Foundry Agent Service and Content Safety
  • Compare Bedrock, Azure AI, direct APIs and self-hosted models, and recommend the right one
Project shipped

Cloud AI Showdown

Fine-tune a small open model to classify ClaimSense documents, then run RegRadar's retrieval on both Bedrock Knowledge Bases and Azure AI Search, add Bedrock Guardrails and Azure AI Content Safety, and publish a side-by-side comparison of quality, speed and cost.

Who this sprint is for

Your Gen-AI apps work on your laptop but you've never deployed one for real traffic, or you don't know what breaks when thousands of users arrive at once.

You'll learn
  • — Deployment options — public APIs, Bedrock / Azure AI and self-hosted models, and cost per million tokens
  • — Serving models with vLLM — your Sprint 5 fine-tuned model behind an OpenAI-compatible server; batching, quantization, benchmarks
  • — Model gateway with the LiteLLM proxy — routing, fallbacks, team API keys and cost tracking
  • — Planning for millions of users — 10M requests/day ≈ 116 RPS average and ~580 at peak
  • — Load handling — per-customer rate limiting with Redis, exact / semantic / prompt caching, and queues
  • — Decision models as a scale lever — moving classification, routing and verification off the LLM path
  • — The circuit breaker pattern — closed, open and half-open states, thresholds, timeouts and shared state in Redis
  • — Combining circuit breakers with retries, backoff and rate limits so they don't fight each other
  • — Cloud deployment, your choice — AWS (Bedrock, ECS Fargate, ALB, ElastiCache, SQS, RDS, CloudWatch) or Azure (Azure OpenAI, AI Search, Container Apps, Service Bus, PostgreSQL, Monitor)
  • — Kubernetes with autoscaling, CI/CD with GitHub Actions, evaluation gates, canary releases and fast rollback
By the end you can
  • Deploy LLMs and Gen-AI applications to Kubernetes and the cloud
  • Build a capacity plan for millions of requests a day
  • Protect your system under heavy load with rate limiting, caching, queues and circuit breakers
  • Release safely, monitor what matters, and prove performance with load tests
Project shipped

TokenGrid — Million-User LLM Platform (part 1)

Serve your fine-tuned model, put a gateway with rate limiting, caching and circuit breakers in front of every model, deploy to AWS or Azure through CI/CD, and load-test it. TokenGrid is presented and defended in Sprint 7.

Who this sprint is for

Everyone. This is where everything you've built becomes everything you can defend — most courses end with a project; this cohort ends with a public Demo Day.

You'll learn
  • — Capstone completion — finish TokenGrid and put RegRadar, ClaimSense or TripPilot behind it
  • — Capstone architecture review — a one-on-one review run exactly like a senior interview round
  • — Demo Day — present your platform live to peers, alumni and invited hiring partners
  • — Final mock interview — a full senior loop: resume deep-dive, system design, client case and behavioural
  • — Live system design rounds — from requirements to architecture to scale
  • — Client-facing skills — discovery calls, scoping a pilot, success metrics and demos to non-technical stakeholders
  • — Resume rewrite with honest, quantified outcomes, and keyword matching for each job description
  • — GitHub portfolio polish — READMEs, architecture decision records, demo videos and pinned repos
  • — LinkedIn optimisation, behavioural prep with STAR, and a salary negotiation playbook for India and remote roles
By the end you can
  • Present and defend a production Gen-AI platform end to end
  • Walk into system design rounds with confidence
  • Apply with a tailored resume, a polished GitHub and a live demo
  • Negotiate your offer from a position of clarity
Project shipped

TokenGrid — completed, presented and defended on Demo Day

Your flagship platform, built across Sprints 6 and 7 — exactly how real engineering teams ship platforms — presented live and defended in a senior-style architecture review.

Enroll Now — ₹20,000
View Full Syllabus
What you ship

Five production systems on your GitHub by Demo Day.

Project 01

LedgerLens — GST Invoice Intelligence API

Turns messy invoices and receipts — PDFs and phone photos — into validated, GST-ready data.

Used for accounts-payable automation at CA firms, fintech lenders, expense-management platforms and SME accounting tools.
FastAPILangChainLangSmithPydanticOpenAIClaudeOllamaPostgresRedisDockerGitHub Actions
Why this gets you hiredProves you can use LangChain to make LLM output reliable — structured outputs, validation, confidence scoring, human review, model fallback and cost control — the questions every Gen-AI interview starts with.
Project 02

RegRadar — Compliance Copilot for Indian Fintech

Answers compliance questions from RBI and SEBI circulars and company policies — with exact citations.

Used by compliance and risk teams at banks, NBFCs, fintechs and brokerages — and in every enterprise knowledge assistant.
FastAPILangChainPineconepgvectorDoclingBM25Cross-encoderRedisRAGASDeepEvalDocker
Why this gets you hiredCovers every major RAG interview topic — ingestion, chunking, hybrid search, reranking, citations, multi-tenancy, permissions, evaluation and cost — on a problem with real business value.
Project 03

ClaimSense — Health Insurance Claims Automation Engine

A durable agentic workflow that processes thousands of claims a day without losing one.

Used in claims processing at health insurers and TPAs — the same pattern powers loan processing and KYC onboarding.
LangGraphLangChainFastAPIPostgres checkpointerRedisCelery / ArqTypeSafe AI JevPresidioLangfuseHugging Face
Why this gets you hiredShows you can build agentic workflows that are durable, auditable and high-volume — state design, human-in-the-loop, queues, idempotency and LLM-plus-rules design.
Project 04

TripPilot — Multi-Agent AI Travel Concierge

Type "Goa, 4 days, ₹40k, two people" — a team of AI agents plans, prices and books the whole trip.

The agentic pattern behind travel apps like MakeMyTrip and corporate travel desks — and every AI assistant that takes real actions for users.
LangGraphMCP Python SDKA2ATypeSafe AI JevCrewAI / MS Agent FrameworkFastAPIGuardrails AIpromptfooRedisOpenTelemetry
Why this gets you hiredDemonstrates multi-agent design, MCP and A2A, a fast decision layer, multi-framework skills and agent security in one system — and it's a project any interviewer instantly understands, because everyone has planned a trip.
Project 05

TokenGrid — Million-User LLM Platform (Flagship)

The platform that serves, routes, protects and scales every LLM call — built for 10 million requests a day.

Used by internal AI platform teams at large product companies and GCCs running LLMs for many teams or customers.
vLLMLiteLLMTypeSafe AI JevFastAPIRedisKubernetesHelmKEDAPrometheusGrafanaLangfusek6AWS / Azure
Why this gets you hiredAnswers the question that separates senior candidates — "How would you deploy and scale this for millions of users?" — with a system you actually built, deployed and load-tested.

All five projects: containerised, tested, evaluated, observable and deployable — each with an ADR, a live demo and a 2-minute video.

Your instructor

Taught by a Sr. Gen-AI Developer who actually ships.

Prateek Mishra

Sr. Gen-AI Developer Working at a global MNC
Founder & instructor · TechSimPlus
Verified instructor profile on LinkedIn
12000+
Engineers taught
8
Years Of Experience
20+
Production projects

I build production Gen-AI systems for a living and have shipped what most courses only describe in slides — agentic RAG pipelines on AWS Bedrock, MCP servers in production, multi-agent systems with stateful checkpointing.

I started TechSimPlus because the gap between tutorial-grade Gen-AI and what actually ships in production was getting wider, not smaller. 12,000+ engineers later, that gap is still there. Vector 2.0 closes it.

I'm not a full-time creator. I'm a practitioner who teaches — and I trained the Vector 1.0 cohort. Every project in Vector 2.0 is something I have architected at work or for clients, rebuilt for how Gen-AI actually runs in production in 2026.

Read Prateek Mishra's full profile →
Stack I ship in production
  • FastAPI
  • LangChain
  • LangGraph
  • MCP
  • A2A
  • Production RAG
  • Fine-Tuning
  • AWS Bedrock
  • Azure AI
  • vLLM
  • Kubernetes
  • LLMOps
Find me on YouTube · 10K+ LinkedIn · 2K+ techsimplus.com
Mock Interviews

The five rounds that will save your senior interview.

Five one-on-one mock interviews across Vector 2.0, each followed by detailed written feedback. Difficulty is matched to your experience level.

Round metric
5
1:1 mock interviews
Round metric
Real
Production-grade questions
Round metric
24h
Written feedback turnaround
Round metric
Senior
Calibrated to senior rounds
Round 01 of 05After Sprint 2

LLMs + Production RAG

LLM foundations and production RAG — defend your retrieval, reranking and eval choices.

  • How LLMs work
  • Hybrid search
  • RAG evals
Written feedbackwithin 24h
Round 02 of 05After Sprint 3

Agentic Workflow Design

Design a durable, high-volume LangGraph workflow under hostile follow-ups.

  • State design
  • Durability
  • Human-in-the-loop
Written feedbackwithin 24h
Round 03 of 05After Sprint 4

Agents, MCP, A2A & Security

Multi-agent architecture, protocol choices and how you'd threat-model it.

  • MCP vs A2A
  • Tool design
  • Red-teaming
Written feedbackwithin 24h
Round 04 of 05After Sprint 6

Cloud AI & Scale

Fine-tuning, AWS Bedrock, Azure AI, LLM deployment and scaling to millions of users.

  • Fine-tuning
  • Bedrock / Azure
  • Circuit breakers
Written feedbackwithin 24h
Round 05 of 05Sprint 7

Final Senior Loop

Full loop — resume deep-dive, system design, client case and behavioural.

  • Resume defense
  • System design
  • Client case
Written feedbackwithin 24h
Round 06 = the real one After Demo Day

Your real interview

After five rehearsals with written feedback, your actual interview is just the sixth rep — not the first. That's the goal.

You walk in calibrated.
Five rehearsed rounds.One real one. Walk in calibrated.
Format

Live cohort. Real milestones. Lifetime access.

Morning batch

Tuesday & Thursday — 6:30 – 8:30 AM IST, live

Evening batch

Tuesday & Thursday — 8:00 – 10:00 PM IST, live

Saturday - Sunday

Both batches · 10:00 AM – 4:00 PM IST — deep work, builds and capstone reviews

Total

2.5 – 3 months · ~16 hrs/week live · live + recorded

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✓Private Discord — lifetime access
✓5 one-on-one mock interviews
✓Lifetime access to recordings
✓Demo Day with hiring partners
For working professionals

Built around a full-time job

Choose a morning or evening batch

Weekday live sessions run Tuesday & Thursday — 6:30 – 8:30 AM IST or 8:00 – 10:00 PM IST. Pick the batch that fits around work.

Every session recorded, lifetime access

Recordings are up within 24 hours and stay with you for life — a busy week at work never leaves you behind.

Weekends are build time with reviews

Saturday & Sunday, 10:00 AM – 4:00 PM IST, is for building your projects — with reviews and feedback on your work.

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Pricing

Early-bird fee. Till 30 Oct 2026.

Early-bird fee · Vector 2.0
Vector 2.0
150 engineers per batch
₹20,000
₹25,000
Save ₹5,000
Early-bird fee for registrations till 30 Oct 2026. Regular fee ₹25,000 after that.
Early-bird till
30 Oct 2026
₹20,000
Last date
11 Nov 2026
₹25,000
Course starts
21 Nov 2026
Live cohort
Early-bird fee ends in
25
Days
07
Hours
14
Min
07
Sec
Everything included
  • 2.5 – 3 months of live cohort sessions (~16 hrs/week)
  • Self-paced pre-work — Python, Docker and Git
  • Launchpad — production FastAPI starter kit
  • 5 production-grade Gen-AI projects with architecture docs
  • Fine-tuning, AWS Bedrock and Azure AI sprint
  • Decision models built into three of the five projects
  • Cloud deployment track of your choice: AWS or Azure
  • 5 one-on-one mock interviews with written feedback
  • Demo Day with invited hiring partners
  • Bonus self-paced module: Career Accelerators
  • All recordings with lifetime access
  • Private Discord community — lifetime access
  • Direct DM access to the instructor during the cohort
  • Resume rewrite, GitHub polish & LinkedIn audit
  • Job referrals when openings cross our network
  • Career support after the cohort + certificate
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About Vector 2.0 — the Gen-AI Developer course by Prateek Mishra

Vector 2.0 is a live, project-led online Gen-AI Developer course by TechSimPlus, taught by Prateek Mishra, a Sr. Gen-AI Developer with 8+ years of experience, 20+ production AI projects and 12,000+ engineers taught. It is the upgraded edition of Vector 1.0 and runs for 2.5 – 3 months. The cohort starts on 21 Nov 2026, registration closes on 11 Nov 2026, with 150 engineers per batch. The course fee is ₹25,000, and the early-bird fee is ₹20,000 on or before 30 Oct 2026.

Across eight sprints you learn FastAPI, how LLMs work, LangChain, production RAG (Pinecone, pgvector, Qdrant, RAGAS, DeepEval), agentic workflows with LangGraph, AI agents with MCP and A2A, CrewAI and Microsoft Agent Framework, decision models, LLM fine-tuning with LoRA and QLoRA, AWS Bedrock, Azure AI and LLMOps — vLLM, LiteLLM, Kubernetes, caching, rate limiting and circuit breakers for millions of users. You ship five production projects — LedgerLens, RegRadar, ClaimSense, TripPilot and TokenGrid — clear five one-on-one mock interviews with written feedback, and present on Demo Day to invited hiring partners.

It is built for backend, software, data, QA and DevOps developers in India, the UAE, the UK, the US and Singapore who can write Python and want AI Engineer, Gen-AI Developer, Agentic AI Engineer, LLM Engineer or AI Platform Engineer roles. The only prerequisites are Python, Docker and Git. Classes are live on Tuesday and Thursday plus Saturday and Sunday (10 AM – 4 PM IST), and every session is recorded with lifetime access.