AI Engineer • Generative AI • LLM Systems • RAG • AI Agents
Building intelligent AI-powered applications, retrieval systems, and production-ready Generative AI solutions using LangChain, LangGraph & Vector Databases.
About Me
I'm an AI Engineer with 2+ years of hands-on experience building production-grade AI systems. I specialize in Generative AI, RAG pipelines, LLM workflows, and agentic AI architectures using Python, LangChain, and LangGraph.
Passionate about Data Science, Machine Learning, and Analytics — I cover the full AI engineering stack: embedding pipeline design, vector database integration (FAISS, ChromaDB, Pinecone), multi-agent orchestration, and FastAPI backend development. I've applied these skills in healthcare, building systems that process medical documents with high accuracy at scale.
Open to AI Engineer, Data Scientist, and ML Engineer opportunities. I approach every project with a production-first mindset — writing clean, scalable code and engineering AI systems that solve real-world business problems.
AI Engineer Tech Stack
Skills & Technologies
AI / ML Frameworks
LLM Providers
Vector DB & Backend
Languages & Tools
Technical Depth
Core Expertise
Deep technical skills across the full AI and data science stack — from model building to production deployment.
Generative AI
Machine Learning
Data Science
AI Development
What I Offer
Services
Production-grade AI engineering services — from LLM integration to full agentic system architecture.
AI & LLM Application Development
End-to-end development of production-grade AI applications powered by OpenAI, Gemini, and open-source LLMs with robust prompt engineering.
RAG System Architecture
Design and implementation of Retrieval-Augmented Generation pipelines using FAISS, ChromaDB, and Pinecone for accurate, grounded AI responses.
AI Agent Development
Multi-step agentic AI systems built with LangChain and LangGraph — capable of reasoning, tool use, memory, and autonomous task execution.
FastAPI Backend Development
High-performance FastAPI backends for AI services, with structured endpoints, async processing, and seamless LLM integration.
Vector Database Integration
Embedding pipeline design, chunking strategies, and vector store setup for scalable semantic search and knowledge retrieval systems.
AI Workflow Automation
Automated AI pipelines connecting LLMs, APIs, and data sources — reducing manual effort and enabling intelligent process automation.
Prompt Engineering Systems
Systematic prompt design using few-shot, chain-of-thought, and structured output techniques to maximize LLM reliability and accuracy.
Machine Learning Solutions
Supervised and unsupervised ML model development — classification, regression, and predictive analytics tailored to business needs.
LLM Fine-Tuning & Integration
Custom fine-tuning of open-source LLMs on domain-specific data and seamless integration into existing applications and workflows.
AI SaaS Product Development
Full-stack AI product development from architecture to deployment — scalable, production-ready, and built for real-world usage.
Work Experience
2+ Years AI Engineering
AI Engineer
BYTECHNIK LLC
- Architected production RAG pipelines using FAISS and ChromaDB for medical document retrieval at scale
- Built LangChain-based conversational AI workflows with persistent memory and multi-turn context management
- Implemented LangGraph agent orchestration for complex multi-step reasoning and autonomous task execution
- Integrated OpenAI and Groq APIs with structured prompt engineering strategies to minimize hallucinations
- Designed embedding pipelines using Hugging Face transformer models for semantic document search
AI Engineer
METACAPS IT SOLUTIONS
- Designed and deployed production RAG systems with Pinecone vector store and OpenAI APIs for enterprise clients
- Built high-performance FastAPI backends serving AI inference endpoints with async processing and rate limiting
- Developed prompt engineering strategies using few-shot, chain-of-thought, and structured output techniques
- Automated AI workflows reducing manual processing time by 60%+ through intelligent pipeline orchestration
- Architected multi-agent AI systems using LangGraph for complex, stateful task automation at production scale
What I've Built
Projects
Production-grade AI systems — from RAG pipelines and LLM agents to ML models and data-driven solutions.
AI Lab Report Summarizer
Production AI system that summarizes medical lab reports using LangChain and OpenAI. Extracts clinical findings with structured output parsers and a FastAPI backend designed to minimize hallucinations in healthcare outputs.
- Structured clinical extraction
- Hallucination reduction
- Async document processing
Reduced manual report review time by 70% for healthcare teams.
AI Medical Conversation Summarizer
Intelligent system that converts raw doctor–patient conversation transcripts into structured clinical summaries using LangChain and OpenAI GPT models.
- Conversation parsing
- Clinical NLP
- Structured JSON output
Automated clinical documentation for 100+ conversation records.
AI SOAP Notes Generator
Automated clinical documentation system generating structured SOAP notes from medical conversations. Built with OpenAI, LangChain output parsers, and a FastAPI backend for reliable, formatted medical documentation.
- SOAP format enforcement
- Output parsers
- Prompt-driven structure
Cut clinical documentation time by 60% for medical practitioners.
RAG-Based AI Chatbot
Multi-document retrieval AI system with LangChain and FAISS. Implements embedding and chunking pipelines for a scalable knowledge base with conversational memory and multi-turn agent orchestration.
- Conversational memory
- Multi-document retrieval
- Semantic chunking
Enabled accurate Q&A over 500+ document pages with sub-second retrieval.
EMitra AI RAG Chatbot
Domain-specific RAG system for eMitra (Rajasthan government portal) that answers citizen queries from official documentation using LangChain and Pinecone vector store.
- Government document ingestion
- Semantic search
- Multi-turn chat
Provided accurate answers to 95%+ citizen queries from official documents.
EMR Multiagent AI Chatbot
Conversational EMR Multiagent AI Chatbot with persistent memory using LangChain, LangGraph, and OpenAI. Supports multi-turn context management and tool-augmented responses.
- Persistent memory
- Tool use
- Multi-turn context
Deployed as production chatbot handling 200+ conversations daily.
Heart Disease Prediction Model
Machine learning classification model that predicts heart disease risk from patient health indicators using Scikit-Learn. Includes full EDA, feature engineering, and model evaluation pipeline.
- Feature engineering
- Cross-validation
- ROC-AUC evaluation
Achieved 88% accuracy in early heart disease risk prediction.
Appointment Best Slot Predictor Model
ML model that predicts optimal appointment slots based on historical booking patterns, user behavior, and time-series analysis using Pandas and Scikit-Learn.
- Time-series analysis
- Booking pattern recognition
- Slot ranking
Improved appointment utilization rate by 35% through optimal slot suggestions.
Next-Word Prediction Model
Deep learning NLP model for next-word prediction built with TensorFlow and LSTM architecture. Trained on custom text corpus with embedding layers and sequence modeling.
- LSTM sequence modeling
- Custom embeddings
- Text preprocessing
Demonstrated 82% top-3 accuracy on held-out text prediction tasks.
Multi-Agent Research System
Advanced research assistant system using LangGraph to coordinate multiple specialized AI agents for scientific literature analysis, data extraction, and hypothesis generation.
- Multi-agent orchestration
- Research paper parsing
- Hypothesis generation
Accelerated academic research workflow by automating literature review and data synthesis.
Movie Recommendation System
Collaborative filtering and content-based recommendation engine that suggests movies based on user preferences, viewing history, and semantic analysis of movie metadata.
- Hybrid recommendation
- User profiling
- Movie clustering
Achieved 85% accuracy in predicting user movie preferences for personalized recommendations.
AI Video Assistant
Multimodal AI system that processes video content to generate summaries, extract key insights, and answer questions about video content using vision-language models.
- Video transcription
- Scene analysis
- Content summarization
Reduced video content analysis time by 90% while maintaining high information fidelity.
Movie Information Extractor
NLP-powered system that extracts structured information from movie reviews, synopses, and metadata to build comprehensive movie databases with sentiment analysis.
- Named entity recognition
- Sentiment analysis
- Information extraction
Automated extraction of 10,000+ movie attributes with 92% accuracy for database population.
AI Mode Chatbot
Context-aware chatbot with dynamic mode switching between casual conversation, technical assistance, and creative brainstorming modes using LLM routing and prompt engineering.
- Mode switching
- Context preservation
- Persona adaptation
Improved user engagement by 45% through intelligent mode selection based on conversation context.
RAG Book Assistant
Retrieval-Augmented Generation system for book analysis that answers questions, summarizes chapters, and generates study guides from personal book collections using vector search.
- Book chunking
- Semantic search
- Chapter summarization
Enabled deep analysis of 50+ technical books with accurate, citation-aware responses.
AI Social Media Idea Generator
Creative AI assistant that generates viral social media content ideas, captions, and post strategies tailored to specific platforms, audiences, and brand voices.
- Platform-specific optimization
- Audience targeting
- Trend analysis
Increased social media engagement by 65% through AI-generated content strategies.
Academic Background
Education
Building a strong technical foundation alongside real-world AI engineering experience.
Bachelor of Computer Applications (BCA)
IPS College
Pursuing BCA with a focus on Computer Science fundamentals, programming, and software engineering. Actively building production AI projects and applying real-world development skills alongside professional AI engineering work.
Senior Secondary — Science (PCM)
Shri Pragya Senior Secondary Public School
Completed Class 11th–12th in the Science stream with PCM. Achieved 84% in board examinations, building strong analytical and problem-solving foundations that directly support AI/ML work.
Secondary Education (Class 10th)
Shri Pragya Senior Secondary Public School
Completed Class 9th–10th with strong academic performance. Achieved 83% across 6 subjects in the Class 10th board examinations with distinction in Mathematics and Science.
Client Feedback
Testimonials
What collaborators and clients say about working with me on AI engineering projects.
“Sadik has been an outstanding AI engineer on our team. His ability to architect and deliver production-grade RAG pipelines and LLM workflows is remarkable for someone at his stage. He brings strong technical depth, ownership, and a problem-solving mindset to every project.”
Liyakat Hussain
CEO, BYTECHNIK LLC
Get In Touch
Contact Me
Open to AI engineering roles, freelance projects, and collaborations. Let's build something intelligent together.
Let's Work Together
I'm actively looking for AI engineering opportunities where I can build RAG systems, LLM workflows, and agentic AI products that solve real-world problems.
Find me on
GitHub
SadiqCodex
in
sadiq_ali.10
Twitter / X
@SadikRangrej10
Hugging Face
SadikMohammad
sadiqrangrej10@gmail.com