⚡ Systems & Generative AI Engineering

Mohsin Nisar

An ambitious AI engineer dedicated to turning probabilistic, unpredictable language models into deterministic, production-grade software infrastructure.

🚀 My Dream Goal

To spend the next 3–4 years building myself from absolute fundamentals to the level of a senior AI/ML Engineer and AI Systems Researcher — developing the depth to design, train, optimize, and deploy production-grade AI systems at scale.

This is not a short-term goal or a race to learn tools. It is a long-term journey toward mastering the fundamentals, mathematics, algorithms, machine learning, deep learning, generative AI, systems engineering, research, and production infrastructure required to build serious AI systems from the ground up.

While I love building native automation workflows and experimental systems directly within my Linux environment, Currently, I am Intensely working on building genai-50-projects ↗.

01. Structured Outputs & API Integration

Enforcing JSON schemas, system prompts, error handling, and API integration.

02. RAG & Vector Retrieval

Vector databases, chunking strategies, embeddings, and semantic search.

03. Tool Calling & Agent Utilities

Function execution, agent loops, external APIs, and safety constraints.

04. Multi-Agent & State Graphs

LangGraph state machines, multi-role agent coordination, cyclic execution, and supervisor routing.

05. Multimodal & Real-Time Voice

Speech synthesis, audio transcription, vision LLMs, and real-time processing.

06. Production Ops, Evals & Scaling

Latency reduction, testing frameworks, observability, semantic caching, and security.

# Featured Project Roadmap

genai-50-projects ↗

Systematic blueprint roadmap for building production-grade GenAI backends — featuring schema-enforced LLM agents, vectorized PyArrow analytics, and async LangGraph architectures.

PydanticSentence-TransformersOllamaLangGraphPyTorchRedis