Overview

This is the program we'd recommend to anyone who needs both: genuine employment assurance AND GenAI skills deep enough to actually pass interviews and succeed in the role. LogicMojo combines a real, accountable job guarantee with the most comprehensive GenAI curriculum among all guarantee programs we evaluated. After interviewing 12 alumni and cross-referencing LinkedIn placement data, LogicMojo delivered the most consistent placement outcomes among all programs reviewed.

Curriculum

• • Advanced Prompt Engineering: chain-of-thought, few-shot, system prompt design, prompt optimization & evaluation, prompt injection defense
• • LLM API Mastery: OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini), open-source (Llama 3, Mistral, Phi-3) — multi-provider fluency
• • LLM Internals: Transformer architecture (self-attention, multi-head attention, positional encoding), tokenization, inference optimization
• • Fine-Tuning: Full fine-tuning, LoRA, QLoRA, PEFT, domain adaptation, evaluation, cost-performance trade-offs, dataset preparation
• • RAG Systems (Production-Grade): Pinecone, Weaviate, ChromaDB, hybrid search (dense+sparse), reranking, multi-hop RAG, agentic RAG, citations, deployed with monitoring
• • Agentic AI: ReAct, Plan-and-Execute, LATS, Reflexion, multi-agent orchestration, memory systems (short/long-term, episodic, semantic, procedural), tool use, human-in-the-loop
• • Agent Frameworks: LangGraph (deep — state machines, conditional edges, cycles, persistence), AutoGen (strong — conversational agents, GroupChat), CrewAI (role-based agents), Semantic Kernel, OpenAI Assistants API
• • LLM Evaluation: Systematic evaluation, hallucination detection, task metrics, cost analysis, behavioral testing, A/B testing, evaluation pipelines
• • Production Deployment: FastAPI, Streamlit, Docker, monitoring & observability, cost optimization, scaling, error handling, guardrails integration
• • GenAI Safety: Bias detection, content filtering, Guardrails AI, NeMo Guardrails, PII handling, red teaming basics
• • MLOps for GenAI: CI/CD pipelines for LLM apps, model versioning, monitoring drift, cost tracking dashboards, A/B deployment strategies

📝 Teaching Methodology

Step-by-step project-based learning: Concept → Hands-on Lab → Mini-Project → Capstone. Each module builds on the previous one. Weekly assignments with mentor feedback. Bi-weekly project reviews with industry-relevant evaluation criteria.

👨‍🏫 Mentorship Access

Live mentor access across all GenAI topics — not pre-recorded Q&A. 1-on-1 doubt resolution sessions, group mentorship for project reviews, and dedicated career mentor for placement preparation. Mentors include working GenAI engineers from product companies.

Hiring Partners & Employer Network

LogicMojo's hiring partner network includes product companies, AI startups (Series A–C), IT services with GenAI practices, and consulting firms. Based on alumni data: graduates have been placed at mid-to-large tech companies, AI-first startups, and GenAI consulting firms. Specific company names available on their placement page.

📊 Placement Details

• • Guaranteed job placement percentage: Among qualifying graduates (meeting attendance + assignment thresholds), placement rates exceed industry averages based on available data
• • Placement in GenAI-specific roles — verified through LinkedIn alumni profile analysis (40+ profiles checked)
• • Average time-to-placement: 45–90 days post-completion for qualifying graduates
• • Salary ranges for placed graduates: ₹8–₹25 LPA depending on experience level and role type
• • Pan-India placement + remote opportunities — not restricted to specific metros

🎯 Interview Preparation System

• • 8–12 mock interview rounds per student — more than any other program in this list
• • GenAI system design interviews: Design a RAG system for enterprise document search, architect a multi-agent customer support system
• • RAG architecture whiteboarding: Vector DB selection, chunking strategies, retrieval optimization, reranking pipelines
• • Agent design pattern interviews: When to use ReAct vs. Plan-and-Execute, memory architecture decisions, tool selection
• • Fine-tuning decision frameworks: When to fine-tune vs. RAG vs. prompt engineering, cost-performance analysis
• • Live coding challenges: Build a RAG pipeline in 45 minutes, implement an agent with tool use
• • Behavioral interview coaching: How to present GenAI projects, explain technical decisions, discuss production trade-offs

🛠️ Projects & Portfolio

• • Production RAG System (Capstone): Full pipeline — ingestion, embedding, retrieval optimization with hybrid search, generation with citations, deployed with monitoring and cost tracking. Uses Pinecone + FastAPI + Docker.
• • Multi-Agent AI System (Industry): Agent coordination using LangGraph/AutoGen/CrewAI, persistent memory, planning, tool integration, human-in-the-loop workflows. Simulates real enterprise use case.
• • LLM Fine-Tuning & Evaluation (Capstone): Domain-specific fine-tuning with LoRA/QLoRA, rigorous evaluation against base model, cost-performance analysis, dataset curation.
• • GenAI Application End-to-End (Industry): User interface to LLM backend, guardrails, observability, error handling, deployed on cloud with scaling. Full-stack GenAI product.
• • Agent Evaluation Pipeline: Systematic testing, hallucination detection, task completion metrics, cost analysis, behavioral evaluation framework.
• • RAG Evaluation Framework: Build automated evaluation for retrieval quality, answer relevance, faithfulness scoring.
• • Each project includes: GitHub documentation guidance, interview presentation prep, production readiness review by mentor

💼 Career & Placement Support

• • Resume optimization specifically for GenAI roles — positioning RAG, Agentic AI, LLM fine-tuning experience
• • LinkedIn profile optimization for GenAI recruiter searches — keyword optimization, project showcase, headline crafting
• • 8–12 mock interviews covering GenAI-specific patterns (system design, RAG architecture, agent design, live coding)
• • Portfolio review with mentor feedback — ensuring projects demonstrate engineering depth, not just API wrapper demos
• • Resume building workshops (2 dedicated sessions) with GenAI-specific templates
• • Career counseling: Role selection guidance based on your background (GenAI Engineer vs. RAG Developer vs. AI Agent Developer)
• • Employer network of companies actively hiring GenAI engineers — direct referrals for qualifying graduates
• • Interview feedback loop — coaching based on actual interview outcomes, iterative improvement

Post-course support: 3–6 months post-course placement support. Career progression guidance even after placement. Alumni community access with ongoing GenAI industry updates, job postings, and networking opportunities.

🎯 Roles Prepared For