AI Engineer with 1+ years in Scalable ML Backends & Generative AI
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AI Software Engineer specializing in scalable ML backends and Generative AI. With deep expertise in engineering local-first RAG architectures, I help enterprises deploy production-ready LLMs while maintaining absolute data privacy and optimizing hardware constraints. My focus bridges rigorous data science methodologies with robust backend orchestration to build context-aware workflows that solve practical business challenges.
Szabist Islamabad
Masters of Data Science · Data Science
August 1, 2025 – Present
Iqra University Karachi
Bachelors of Computer Science · Computer Science
N/A – June 30, 2024
Cognetex
Ai Engineer
March 1, 2025 – Present
India
Aptech
Data Analyst
June 1, 2024 – December 1, 2024
India
Enterprise RAG-Ops Platform – Knowledge Assistant
June 24, 2026 – Present
Architected a local-first RAG platform on Kubernetes (K3d) utilizing Ollama and ChromaDB, enabling secure internal knowledge retrieval for 3 concurrent users with sub-800ms latency. Engineered a FastAPI gateway and automated data pipeline for file ingestion, processing 8 documents while delivering real-time context-aware streaming. Deployed MinIO and PostgreSQL for secure object storage and structured logging, managing 3.4 GBs of raw data and achieving 100% auditability of all LLM interactions. Integrated Grafana to monitor vector counts and RAG generation latency, reducing system debugging time by 40%. Automated CI/CD workflows via GitHub Actions for Docker builds and Kubernetes updates, decreasing deployment times from hours to minutes
ContexAI - RAG based Monitoring System
June 24, 2026 – Present
Engineered an enterprise document intelligence platform capable of parsing PDFs, Excel files, and archives, automating the ingestion and embedding process for 50+ documents. Implemented a hybrid chunking strategy (semantic, overlapping, and sliding window), optimizing context preservation and increasing vector retrieval accuracy by 20%. Built local vector search pipelines utilizing ChromaDB and BGE embeddings with custom reranking mechanisms, boosting query precision by 25%. Developed semantic search and structured extraction tools, accelerating enterprise knowledge discovery and reducing manual document review time by 43%.
NLP-Focused Resume Analyzer
June 24, 2026 – Present
Engineered an NLP-driven resume analysis pipeline utilizing TF-IDF keyword weighting and similarity matching, automating candidate scoring and reducing manual screening time by 60%. Implemented custom Named Entity Recognition (NER) models to extract 1200+ specific technical skills, generating data-driven candidate evaluations that improved matching accuracy by 30%. Prototyped and validated the end-to-end system via a Streamlit interface, processing a test dataset of 895 resumes and achieving a parsing accuracy of 93%.
Cultural Fit Analysis
The candidate's project diversity, ranging from enterprise RAG platforms to NLP-driven resume analyzers, indicates a broad interest in AI applications. Their experience in both a Data Analyst and AI Engineer role, along with ongoing Master's education, shows a commitment to continuous learning and adaptability. The volunteering activities suggest a community-oriented mindset. The focus on local-first RAG and optimizing hardware constraints aligns with practical, resource-conscious engineering, which can be a strong cultural fit for organizations valuing efficiency and data privacy.
Soft Skills & Operational Fit
The candidate's resume demonstrates strong problem-solving skills through the development of optimized RAG systems and NLP pipelines. Their ability to architect and engineer complex systems, coupled with a focus on performance metrics and auditability, indicates a detail-oriented and results-driven approach. The experience in training end-users also suggests good communication and collaboration skills, which are valuable for operational fit.