AI Academy — From Learning to Leading

Train to become an elite AI Engineer, Agentic AI Developer, or AI Infrastructure Architect with our immersive, production-grade bootcamp tracks.

Explore Tracks

Professional Training Curriculums

12 WEEKS

AI Engineering Bootcamp

Comprehensive foundation covering prompt injection defense, custom LLM fine-tuning, RAG system construction, and deployment.

₹45,000 INR Tuition
  • 12 complete production projects
  • 1-on-1 industry mentor review
  • Official Psivex AI Engineer certification
Outcome: Ready for Senior AI roles
W1-2 Foundations of AI Engineering
  • Python for ML: NumPy, Pandas, scikit-learn deep dive
  • Neural network fundamentals & backpropagation
  • Introduction to Transformer architecture
  • Setting up ML development environments
W3-4 LLM API Mastery
  • OpenAI, Anthropic & Google API integration
  • Prompt engineering: chain-of-thought, few-shot, ReAct
  • Token economics & cost optimization strategies
  • Streaming responses & async patterns
W5-7 RAG Systems & Vector Databases
  • Embedding models: OpenAI, Cohere, sentence-transformers
  • Vector stores: Pinecone, Weaviate, ChromaDB, Qdrant
  • Chunking strategies & retrieval optimization
  • Hybrid search: dense + sparse retrieval fusion
  • Building production RAG with LangChain & LlamaIndex
W8-9 Production Backend & APIs
  • FastAPI for ML services: async endpoints, middleware
  • Authentication, rate limiting & API key management
  • Docker containerization & CI/CD pipelines
  • Monitoring: LangSmith, Weights & Biases integration
W10-12 Capstone & Deployment
  • End-to-end AI product: planning → shipping
  • Cloud deployment: AWS SageMaker, GCP Vertex AI
  • Performance benchmarking & load testing
  • Portfolio project review & career prep
SG
Siddharth Gupta
Lead AI Architect
16 WEEKS

Agentic AI Systems

Advanced multi-agent framework architectures using LangGraph, CrewAI, and AutoGen. Design autonomous self-healing software loops.

₹38,000 INR Tuition
  • Stateful agent communication patterns
  • Tool execution & self-reflection systems
  • Agentic debugging & observability pipelines
Outcome: Agentic Systems Architect
W1-4 Agent Foundations
  • ReAct, Plan-and-Execute & Reflexion patterns
  • Tool use: function calling, structured outputs
  • Memory systems: short-term, long-term, episodic
  • Agent evaluation frameworks & benchmarks
W5-8 Multi-Agent Orchestration
  • LangGraph: stateful workflows & conditional routing
  • CrewAI: role-based agent teams & delegation
  • AutoGen: conversational multi-agent patterns
  • Agent communication protocols & message passing
W9-12 Advanced Agent Patterns
  • Self-healing agents: error recovery & retry strategies
  • Hierarchical task decomposition & planning
  • Human-in-the-loop: approval gates & escalation
  • Agentic RAG: iterative retrieval & synthesis
W13-16 Production Agentic Systems
  • Observability: tracing agent decision chains
  • Cost control: token budgets & circuit breakers
  • Security: prompt injection defense, sandboxing
  • Capstone: autonomous research & coding agent
AR
Dr. Ananya Rao
Senior Agent Researcher
14 WEEKS

LLM Engineering

Deploy high-performance open-source models (Llama-3, Mistral) securely. Optimize token throughput using vLLM, DeepSpeed, and Triton.

₹35,000 INR Tuition
  • Kubernetes GPU node clustering
  • Quantization formats (AWQ, GPTQ, GGUF)
  • Ultra-low latency model caching
Outcome: ML Platform Engineer
W1-4 LLM Internals & Architecture
  • Transformer deep dive: attention, KV-cache, positional encoding
  • Tokenization: BPE, SentencePiece, tiktoken
  • Model families: Llama 3, Mistral, Gemma, Qwen
  • Benchmarking: MMLU, HumanEval, MT-Bench
W5-8 Quantization & Optimization
  • Quantization formats: GPTQ, AWQ, GGUF, bitsandbytes
  • vLLM: PagedAttention, continuous batching
  • TensorRT-LLM & NVIDIA Triton Inference Server
  • Speculative decoding & KV-cache optimization
W9-14 Infrastructure at Scale
  • Kubernetes GPU node scheduling & resource quotas
  • Multi-GPU serving: tensor parallelism, pipeline parallelism
  • Model registry & versioning: MLflow, W&B
  • A/B testing & canary deployments for models
MK
Manoj Kumar
Lead DevOps Engineer
18 WEEKS

AI Infrastructure Engineering

Build the systems that power AI at scale. GPU clusters, inference optimization, model serving, and MLOps pipelines.

₹36,000 INR Tuition
  • Distributed computing scaling models
  • GPU clustering network fabrics setup
  • Parallel training architectures
Outcome: AI Infrastructure Architect
W1-6 Cluster Ops & Networking
  • GPU node scheduling in Kubernetes & resource quotas
  • Multi-node network fabrics: InfiniBand vs RoCE
  • Storage caching architectures for large weights
W7-12 Distributed Training Systems
  • Megatron-LM & DeepSpeed distributed configurations
  • Tensor parallelism, pipeline parallelism, ZeRO stages
  • Debugging hardware failures & training checkpointing
W13-18 Large Scale Model Inference Optimization
  • vLLM continuous batching and PagedAttention settings
  • TensorRT-LLM and NVIDIA Triton model deployment orchestration
  • Speculative decoding, KV-cache quantization, and FP8 operations
RK
Rajesh Khanna
Principal Cluster Systems Architect
10 WEEKS

AI Product Engineering

From zero to AI-powered SaaS. Product design, AI integration patterns, rapid prototyping, and go-to-market for AI products.

₹32,000 INR Tuition
  • Vercel AI SDK integration
  • Supabase Vector PGVector queries
  • Stripe usage-based billing structures
Outcome: Lead AI Product Builder
W1-5 AI Prototyping & SDKs
  • Vercel AI SDK & Next.js App Router integrations
  • Streaming responses, streaming chat UI, tool rendering
  • Supabase vector store setup & PGVector matching
W6-10 Scaling & Stripe Invoicing
  • Stripe usage-based billing setup for API credits
  • Optimizing token latency & multi-region edge caches
  • LLM security validation & user safety layers
PS
Preeti Sen
Director of AI Products
24 WEEKS

Research Engineer Fellowship

Work on original AI research, publish papers in NeurIPS/ICML, and join a cohort of the world's most ambitious systems engineers.

₹48,000 INR Tuition
  • Original conference publication track
  • PyTorch custom layer optimization
  • Pre-training from scratch using JAX
Outcome: Elite AI Research Scientist
W1-4 Advanced Deep Learning
  • Modern architectures: Attention, ConvNeXt, State Space Models
  • Custom PyTorch layers & training loop optimization
  • Benchmarking floating point performance (FP8, FP16, BF16)
W5-12 Model Pre-training & Fine-Tuning
  • Pre-training from scratch on custom clusters using JAX
  • Instruction tuning data generation & evaluation
  • RLHF, DPO & PPO implementation pipeline
W13-24 Thesis Research Project
  • Formulate novel research hypothesis with Psivex advisors
  • Submit paper draft to leading conference tracks (NeurIPS, ICML)
  • Present capstone presentation to hiring research labs
DS
Dr. Sarah Jenkins
Founding Research Director

Tuition Models

Self-Paced Starter

₹15,000 / course

  • Full access to syllabus & assets
  • Discord community access
  • Auto-graded lab exercises
  • Lifetime resource updates
Apply Now

Elite Fellowship

₹45,000 / track

  • All self-paced starter material
  • Weekly 1-on-1 mentorship calls
  • Cap-stone project validation
  • Job placement referral program
  • Psivex Labs certified credentials
Apply Fellowship

Enterprise Scale

Custom / pricing

  • Custom bootcamp tracks
  • Private team cohort channels
  • Dedicated Slack support
  • Advanced evaluation dashboards
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