Meet the Full Team
AI & Machine Learning Engineering
Meet the AI & Machine Learning Team
Architecting generative AI systems, domain-specific RAG pipelines, and intelligent multi-agent orchestration for modern businesses.
Haisem Naeem
Lead AI & ML Engineer
RAG
Pipelines
LLMs
Integrated
Multi
Agent Systems
Fine
Tuned Models
A lead AI & Machine Learning engineer who designs and builds production-grade generative AI systems for enterprise clients. He specialises in Large Language Model (LLM) backends, constructing domain-specific Retrieval-Augmented Generation (RAG) pipelines that enable businesses to query private knowledge bases with zero hallucination. These are real systems deployed into live production environments, not experimental prototypes.
On the multi-agent side, Haisem architects autonomous agent swarms using LangChain and LangGraph, capable of researching, executing code, verifying data, and producing actionable reports without human intervention. He also runs LoRA and QLoRA fine-tuning workflows on PyTorch for domain-specialized classification, information extraction, and text generation tasks — delivering models that outperform general-purpose LLMs on client-specific data.
Large Language Models (LLMs)RAG PipelinesLangChain & LangGraphMulti-Agent SystemsPython & PyTorchVector Databases (Pinecone, Weaviate)OpenAI & Anthropic APIsLoRA & QLoRA Fine-TuningHugging Face TransformersFastAPI & REST APIsPrompt EngineeringAI Workflow Automation
RAG & Knowledge Retrieval
Enterprise search and question-answering over private documents: vector embeddings, semantic chunking, and reranking pipelines that retrieve accurate answers with zero hallucination.
Autonomous Agent Swarms
Multi-agent systems that autonomously research, execute code, verify data, and produce actionable reports. Built with LangGraph for stateful orchestration and reliable inter-agent communication.
Custom Model Fine-Tuning
LoRA and QLoRA fine-tuning for domain-specialized classification, extraction, and generation tasks: delivering models that outperform general-purpose LLMs on client-specific datasets.
AI Integration & Deployment
End-to-end AI deployment via FastAPI and REST APIs, integrating LLM pipelines directly into client applications, dashboards, and business workflows with production-ready reliability.
AI Solutions Team
Muhammad Tayyab
AI Solutions Specialist
Focuses on AI-driven solutions designed to support business growth, smarter workflows, and digital innovation. Contributes to the development and implementation of practical AI applications, intelligent automation, and AI tool integration aligned with real business needs.
AI SolutionsBusiness AutomationAI Tools & WorkflowsAI IntegrationWorkflow OptimizationIntelligent Systems
Turning AI Innovation into Practical Business Solutions
Deploying AI in a business context is far more complex than calling an API. Without structured retrieval pipelines, proper context management, guardrails against hallucination, and production-grade agent orchestration, AI projects fail to deliver consistent, reliable results in live environments.
At RankNex AI, our AI & Machine Learning team ensures every implementation is built on solid engineering foundations — from vector database design and embedding strategies to fine-tuned models and automated multi-agent workflows. Every system is built to run reliably in production, not just in demos.
RAG & Knowledge Systems
Domain-specific retrieval pipelines over private data — semantic search, reranking, and zero-hallucination Q&A at enterprise scale.
Model Fine-Tuning
LoRA and QLoRA fine-tuning on PyTorch for classification, extraction, and generation tasks tailored to client-specific datasets.
Agentic AI Workflows
Autonomous multi-agent systems using LangGraph for stateful orchestration — research, execute, verify, and report without human intervention.