Train to become an elite AI Engineer, Agentic AI Developer, or AI Infrastructure Architect with our immersive, production-grade bootcamp tracks.
Explore TracksLearn to build systems that think — not just respond. AI engineering is probabilistic by nature. This track teaches you to design for that.
Why AI engineering differs from traditional software engineering.
Failure mode taxonomy: hallucinations, distributional drift, latency tails.
Thinking in distributions, not determinism.
Writing evals before writing code.
LLM-as-judge, human preference datasets, behavioral regression.
Building an eval suite that scales with your system.
Designing what enters the context window — the skill that doesn't age.
Schema design for tool/function calls that reduces hallucination.
Typed outputs, structured generation, constrained decoding.
Fallback chains, retry logic, graceful degradation.
Cost dashboards and token budget enforcement.
Capstone: ship with an eval suite, cost dashboard, and reliability runbook.
The hard problem of agents isn't making them act. It's making them act correctly under uncertainty — with real-world consequences.
Planning loops, tool contracts, memory architectures.
ReAct, Plan-and-Execute, Reflexion: reasoning patterns.
Human-in-the-loop as a product decision.
Approval gates, audit logs, rollback mechanisms.
Communication protocols, role separation.
Prompt injection at scale, code execution boundaries.
Token budgets, circuit breakers, observability.
Capstone: autonomous agent with measurable success rate.
Move from model user to model sculptor. Differentiation lives in how you shape their behavior — via fine-tuning, alignment, and evaluation.
Attention, KV-cache, context windows.
Tokenization mechanics, capability benchmarking.
Generating, filtering, curating data.
LoRA / QLoRA, DPO and RLHF.
Constitutional AI, reward modeling.
Red-teaming, building evals.
Economics of intelligent routing.
Capstone: fine-tune a base model for a specific domain.
Build the substrate AI runs on. Not configurations — architectural decisions under real constraints: cost, latency, hardware, team.
Cost model of serving LLMs.
Batching strategies, speculative decoding.
Topology, networking, failure domains.
Tensor parallelism, ZeRO stages.
Latency distributions, token budgets.
Capstone: design a full serving architecture.
The most valuable AI engineer knows two things deeply: AI and their industry. Choose your specialization. Own both sides of it.
Risk modeling, regulatory compliance, fraud detection.
Clinical NLP, HIPAA-compliant pipelines, diagnostics.
Contract analysis, legal research automation.
AI-assisted incident response, self-healing infrastructure.
Engineering at the frontier. Contribute to open infrastructure, write evals that become standards, build tooling researchers use.
State space models, MoE architectures.
Pre-training from scratch on custom clusters using JAX.
Full RLHF, DPO, and PPO implementation.
Preference modeling and reward hacking.
Original research hypothesis, paper draft for NeurIPS/ICML.
Capstone presentation to hiring research labs.
Self-Paced Starter
₹15,000 / course
Elite Fellowship
₹45,000 / track
Enterprise Scale
Custom / pricing
Echo is where you prove what the track taught you. Real-time video, shared code editor, peer evaluation. Not a simulation — a standard. Every track culminates in a live Echo session.
"Candidate effectively explained the trade-offs of micro-agent memory pools."
We don't teach theory. We build engineers from the ground up with production-grade systems thinking.
Every project you build is designed to be shipped. Real users, real infrastructure, real performance requirements.
We implement cutting-edge papers. Our curriculum tracks arXiv weekly and translates research into engineering practice.
Direct weekly sessions with senior engineers from top labs. No TAs. No forums. Real mentorship.
We train you to think in systems, not scripts. Design autonomous agents that reason, plan, and execute at scale.
60+ partner companies actively recruit from Psivex. Direct introductions, not job boards. Average salary: ₹45L+.
Building a startup? Fellows get access to compute credits, co-founder matching, and investor introductions.
Build real, deployable systems. Portfolio projects that actually get you hired.
Real-time interview coaching agent with RAG over company data, behavioral analysis, and real-time guidance.
Multi-step reasoning agent that searches, reads, synthesizes, and writes research reports autonomously.