Autonomous Career Agent (Agentic AI Application)
A multi-agent AI system that automates the job search and application process, demonstrating LLM orchestration and autonomous agent patterns.
Project Overview
My Professional Network is a local-first privacy-preserving analytics platform that transforms raw LinkedIn Takeout data into an interactive dashboard. The project demonstrates full-stack data science skills by combining ETL, NLP, unsupervised learning, and asynchronous web architecture to help users understand and strategically grow their professional networks.
Key Features & Problems Solved
Personal network intelligence from raw platform exports
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Converts static LinkedIn archives into structured, queryable data without relying on third-party SaaS or cloud lock-in.
Semantic role clustering
Uses vector embeddings and unsupervised learning to group thousands of heterogeneous job titles into meaningful professional clusters.
Network structure and reach analysis
Reveals dominant industries, role distributions, and underrepresented areas within a personal network.
Geographic footprint visualization
Maps global connections by resolving ambiguous location strings into precise coordinates.
Local-first, privacy-preserving analytics
All data processing, modeling, and visualization run locally, keeping sensitive career data fully under user control.
Implementation Highlights
Microservices-based local architecture
Orchestrated with Docker Compose, separating web UI, background workers, database, cache, and ML inference services for scalability and isolation.
Asynchronous processing with Celery & Redis
Long-running tasks (parsing profiles, embedding generation, geocoding) execute in the background, keeping the UI responsive.
Local NLP with vector embeddings
Job titles are embedded using a locally hosted Hugging Face inference service, with aggressive caching to reduce recomputation and latency.
Unsupervised clustering pipeline
Combines dimensionality reduction (LSA / SVD) with K-Means, including a custom heuristic for automatic cluster labeling and long-tail handling.
Optimized geocoding strategy
Deduplicates and persists resolved locations to minimize API usage and control operational costs.
Outcomes & Impact
Turned opaque, static LinkedIn exports into a living analytical system.
Demonstrated how data science, backend engineering, and UX can coexist in a single cohesive product.
Showcased trade-offs around performance, privacy, scalability, and maintainability.
Created a foundation for future extensions such as graph databases and conversational network analysis.
Skills Demonstrated
Data Engineering • NLP & Embeddings • Unsupervised Learning • Network Analysis • Asynchronous Systems • Docker & DevOps • Full-Stack Data Science • Privacy-First System Design • System Architecture
Apply This to Your Business
If you have a business problem that requires data-driven solutions, feel free to reach out via contact page to discuss how I can help leverage data science, analytics, and automation to drive value for your organization.
See Also
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