Why Choose These Training Programs
Not slides and theory — patterns from systems that are live in production
Practitioner, Not Presenter
Every topic taught is something actively in use. LangGraph patterns come from Yuvan — a live product with paying schools. RAG chunking strategies come from benchmarks on real student data.
Production Quality Bar
Training covers not just "how to build" but "how to make it trustworthy" — evals, observability, cost engineering, safety nets, and testing patterns for non-deterministic AI systems.
Customized to Your Stack
Training adapts to your team's current tech (Java vs Python, Spring vs FastAPI, AWS vs GCP) and integrates AI patterns into your existing architecture.
AI Training Programs
Built from real production experience, not textbooks
Agentic AI with LangGraph
Topics: LangGraph state machines, multi-agent orchestration (router, supervisor, plan-and-execute), tool/function calling, guardrails and verifier patterns, memory and persistence, re-explain loops, agent observability with LangSmith, evals and production readiness.
Duration: 2–4 days | Level: Intermediate to Advanced
RAG Pipelines and Vector Search
Topics: RAG architecture, document ingestion and chunking strategies, OpenAI / Cohere / BGE embeddings, pgvector + Supabase, hybrid search, top-k retrieval, citation-grounded answers, semantic FAQ caching (cosine similarity), retrieval precision evals, cost optimisation.
Duration: 2–3 days | Level: Intermediate to Advanced
Voice AI Pipelines (OpenAI Realtime + WebRTC)
Topics: OpenAI Realtime API, STT + TTS + VAD + barge-in interruption, WebRTC architecture, ephemeral token patterns, latency benchmarking (p95 first-audio), alternative stacks (Whisper + ElevenLabs, Gemini Live), cost shootouts, server-side vs client-side reasoning.
Duration: 1–2 days | Level: Advanced
AI-Native Backend with FastAPI
Topics: Async FastAPI + Pydantic v2, streaming LLM responses, structured outputs, multi-tenant AI backends, Supabase Auth + RLS, cost-aware request patterns, pytest-asyncio testing for AI services, LangSmith integration.
Duration: 2–3 days | Level: Intermediate to Advanced
LLM Prompt Engineering and Evals
Topics: System prompt design, few-shot and chain-of-thought patterns, structured output with JSON schema, function/tool calling, deterministic vs non-deterministic testing, LangSmith eval frameworks, retrieval-precision metrics, cost vs quality trade-offs.
Duration: 1–2 days | Level: Intermediate
AI-Assisted Development Culture
Topics: Claude Code, Cursor, GitHub Copilot, v0.dev — shipping with AI as a daily collaborator. Code review with AI, AI-driven test generation, rolling out AI tooling to an engineering team, measuring impact.
Duration: 1 day | Level: All levels
Backend and Systems Training Programs
The engineering foundation that makes AI systems reliable at scale
Backend Development with Java and Spring Boot
Topics: Java 21 features, Spring Boot 3.x, Spring Security, Spring Data JPA, Hibernate, RESTful APIs, exception handling, validation, testing with JUnit and Mockito.
Duration: 3–5 days | Level: Intermediate to Advanced
Microservices Architecture
Topics: Microservices design patterns, service decomposition, API Gateway, service discovery, circuit breakers, distributed tracing, event-driven architecture with Kafka, Docker and Kubernetes deployment.
Duration: 3–5 days | Level: Advanced
AWS for Backend and AI Engineers
Topics: AWS Solutions Architect fundamentals, EC2, S3, ECS, Lambda, IAM, RDS, DynamoDB, CloudWatch, VPC, load balancing, auto-scaling, CI/CD with AWS — plus AI services (Bedrock overview).
Duration: 3–5 days | Level: Intermediate to Advanced
High-Level System Design (HLD)
Topics: Scalability principles, distributed systems, load balancing, caching strategies, database sharding, CAP theorem, message queues (Kafka), real-world case studies including AI system design patterns.
Duration: 2–3 days | Level: Advanced
Low-Level Design (LLD) and Design Patterns
Topics: SOLID principles, design patterns (GoF), clean code practices, object-oriented design, UML, code refactoring, interview preparation.
Duration: 2–3 days | Level: Intermediate to Advanced
Database Design and Optimisation
Topics: SQL vs NoSQL, PostgreSQL advanced features, pgvector for AI workloads, Cassandra for scale, Redis caching, DynamoDB, query optimisation, indexing strategies, PgBouncer connection pooling.
Duration: 2–3 days | Level: Intermediate to Advanced
DevOps for Backend Developers
Topics: Docker containerisation, Kubernetes orchestration, CI/CD with Jenkins and GitHub Actions, monitoring with Grafana and ELK, Linux fundamentals, Git best practices.
Duration: 3–4 days | Level: Intermediate
Data Structures and Algorithms
Topics: Core data structures, algorithm complexity, sorting and searching, dynamic programming, graph algorithms, trees and heaps, interview problem-solving techniques.
Duration: 3–5 days | Level: Intermediate
Training Formats
Flexible delivery options to suit your organisation's needs
In-Person Corporate Training
On-site training at your office anywhere in the world. Interactive sessions with hands-on coding.
- Customised curriculum aligned with your tech stack
- Interactive whiteboard sessions
- Team coding exercises and pair programming
- Q&A and real-world problem-solving workshops
- Post-training support
Live Online Training
Virtual instructor-led training via video conferencing. Same quality as in-person with remote convenience.
- Live coding demonstrations and screen sharing
- Breakout rooms for group exercises
- Recording available for review
- Flexible scheduling across time zones
- 30 days post-training email support
Bootcamp-Style Intensive
Intensive 1–2 week programs for rapid skill development. Focus on building production-ready AI or backend projects.
- Project-based learning with real deliverables
- Daily coding challenges and reviews
- Industry best practices embedded throughout
- Portfolio-ready projects to take home
Who Should Attend
Designed for engineers at every level who want to ship real AI systems
Junior Developers (0–2 years)
- Build strong fundamentals in AI-native backend development
- Learn industry-standard tools and AI frameworks
- Understand production code quality standards
- Get a fast path to contributing to AI product teams
Mid-Level Engineers (2–5 years)
- Master LangGraph, RAG, and voice pipelines hands-on
- Learn microservices and distributed AI system design
- Understand LLM cost engineering and eval frameworks
- Prepare for senior / AI lead roles
Senior Engineers (5+ years)
- Deep dive into production agentic system architecture
- Multi-agent orchestration and AI system design patterns
- LangSmith observability and non-deterministic testing
- Strategic AI tooling and team capability building
Engineering Teams
- Standardise AI knowledge and patterns across the team
- Adopt LangGraph, RAG, and voice pipelines efficiently
- Roll out AI-assisted development culture (Copilot, Cursor)
- Improve quality bar with evals and production discipline
Investment and Engagement
Custom proposals based on your team's needs
Pricing Factors
- Training duration (days)
- Number of participants
- In-person vs online delivery
- Customisation requirements
- Travel and accommodation (if applicable)
- Post-training support duration
Get a Custom Quote
Every organisation's training needs are unique. Custom proposals based on:
- Your team's current skill level and tech stack
- Specific AI and backend topics to cover
- Preferred format and timeline
- Business objectives and learning outcomes
Global Availability
- Available for training worldwide
- Remote delivery across all time zones
- In-person available with advance planning
- English as primary training language
- Experience training teams across India, SE Asia, US