Who this is for
Working professionals who already know Python, have some mathematical maturity, use AI natively, and feel pressure to transition into serious AI engineering before the market moves past them.
Advanced AI Placement Sprint to AI-Native Engineering Sprint
AI-Native Engineering
The hardest AI/ML cohort for working professionals who want to become AI-native engineers.
For professionals who feel AI is catching up fast, want to upgrade seriously, and need a structured transition into AI-native engineering roles.
This is not prompt engineering, not a passive course, and not a beginner introduction to programming. It is a dense engineering cohort for working professionals who want to upgrade into AI engineering, reduce career-risk from AI disruption, build systems, evaluate them, defend decisions, and operate under real constraints.
Curriculum Spine
From foundations to production AI systems.
The public map shows the architecture of the sprint. Detailed weekly topic lists stay inside the counsellor-led syllabus review.
Block A
Weeks 1-4
Math + ML Foundations
Probability, statistics, information theory, linear algebra, optimization, classical ML, deep learning, model evaluation, regularization, gradient flow, and engineering judgment.
ProbabilityInformation TheoryLinear AlgebraOptimizationClassical MLDeep Learning
Block B
Weeks 5-7
Transformers + LLM Internals
Sequence models, attention, GPT internals, RoPE, KV cache, MoE, inference, quantization, LoRA, QLoRA, batching, and serving tradeoffs.
AttentionRoPEKV CacheMoELoRAvLLM
Block C
Weeks 8-10
AI Systems + Backend + Data Engineering
GPU architecture, Flash Attention, FastAPI, async systems, Postgres, Redis, queues, Airflow, Kafka, embedding pipelines, and vector database internals.
GPU SystemsFlash AttentionFastAPIPostgresKafkaVector DBs
Block D
Weeks 11-13
LLM Engineering + RAG + Agents
Context engineering, structured outputs, memory systems, retrieval science, GraphRAG, tool calling, agent loops, HITL, durable execution, and dynamic workflows.
Context EngineeringRAGGraphRAGTool CallingAgentsHITL
Block E
Weeks 14-15
Evaluation + Reliability + Security
Golden datasets, slice evaluation, LLM-as-judge, regression gates, tracing, cost dashboards, guardrails, prompt injection defense, red-teaming, and reliability patterns.
Eval HarnessesLLM-as-JudgeTracingGuardrailsRed TeamingSecurity
Block F
Weeks 16-17
RLHF + Agentic RL + Frontier Research
Bellman equations, policy gradients, reward modeling, RLHF, DPO, GRPO, RLVR, reasoning models, test-time compute, diffusion, VAEs, and research taste.
RLHFDPOGRPOReasoning ModelsDiffusionResearch Taste
Block G
Week 18
Production Capstone + Hiring Sprint
A production AI system with architecture decision records, an evaluation harness, deployment, monitoring, cost tracking, a live demo, and public proof-of-work.
CapstoneADRsDeploymentMonitoringCost DashboardDemo
Every Week
The learning loop is the curriculum.
Participants first experience the failure, then study the invention, then build, explain, evaluate, and publish.
Productive Failure
Participants begin inside a broken system, impossible constraint, or misleading output so they feel the problem before learning the solution.
Retrieval Warmup
Older concepts are recalled without notes so understanding compounds instead of disappearing after each week.
Theory + Live Demonstration
The invention is introduced only after the failure is real. Participants watch the thinking, not just the final code.
Guided Build
A scaffold shows how a serious system is structured: interfaces, decisions, tests, logs, and failure cases.
Independent Build
The scaffold disappears. Participants build, debug, evaluate, and defend their own implementation.
Self-Explanation
If a participant cannot explain the tradeoffs, alternatives, and failure modes, the work is not done.
Proof of Work
Learning becomes visible through demos, writeups, technical posts, evaluation reports, and capstone artifacts.
AI-Native Engineering
Coding with AI is a systems discipline.
The cohort teaches participants to design the system around the AI, not merely ask an IDE to generate code.
Designing Loops + Loop Engineering
Design bounded build-measure-correct loops where AI generates, tests, reads failures, improves, and stops under explicit success criteria.
Agentic Workflows
Move beyond one-shot prompting into workflows that perceive, plan, act, observe, recover, escalate, and preserve state across long-running tasks.
Model Delegation
Use smaller or open-source models for mechanical work while reserving frontier models for reasoning-heavy decisions, synthesis, and judgment.
Mixture of Models
Design systems where different models play different roles: planner, executor, critic, verifier, summarizer, retrieval judge, or domain specialist.
Orchestration
Coordinate subagents, tools, queues, retries, memory, logs, and human approvals so the system behaves coherently under changing context.
Guardrails + HITL
Add permission boundaries, policy checks, destructive-action approvals, human review points, fallback paths, and audit trails for serious production work.
Hard Constraint Labs
Participants learn agent reliability under pressure.
Serious agents do not operate in clean tutorial environments. They face partial information, missing capabilities, ambiguous requests, strict policies, token budgets, latency limits, and evaluators that punish inconsistent behavior.
The point is not whether a model can answer once. The point is whether a system behaves reliably when the environment is constrained, noisy, and adversarial.
Missing tools or unsupported capabilitiesAmbiguous user intent and preference resolutionPolicy-constrained tool useToken and latency budgetsVerifier, critic, and retry loopsState, memory, and decision persistenceEvaluation harnesses and regression gatesHuman approval before risky actions
Parallel Rails
Five tracks run through all 18 weeks.
The sprint is built to create engineering depth, research taste, proof-of-work, and opportunity creation at the same time.
AI Coding Systems
Coding agents, AI IDE workflows, terminal-first development, code review, repo understanding, patch verification, and AI-generated code safety.
Paper Club
Read, critique, reproduce the core idea, present the insight, and defend what aged well or failed.
Failure Friday
Study real AI failures: hallucination, tokenization, prompt injection, silent model failure, RAG failure, reward hacking, and cost blowups.
Engineering Judgment
Every week participants defend a real decision: what they would do, why, what tradeoff they make, and what alternative they reject.
Startup Operator + Outreach Engineering
Ship, talk to users, publish proof-of-work, understand markets, build outreach systems, and create opportunity instead of waiting for it.
Detailed Syllabus
Want the exact week-by-week curriculum?
This page shows the curriculum architecture. The full syllabus includes each week's topics, papers, productive failure trigger, build work, self-explanation checkpoint, interview mapping, and deliverables.