Built for AI Research teams to scale LLM inference and manage vector database performance without manual infrastructure management.
Problem
Inference Latency: High-volume API requests to LLMs were resulting in 10-second wait times for users.
Vector DB Bottlenecks: Poorly optimized indexing in Pinecone was causing inaccurate "hallucinations" in RAG (Retrieval-Augmented Generation) results.
Deployment Friction: Data scientists had no automated way to push new model versions or prompts to production safely.
GPU Cost Inefficiency: GPU clusters were remaining active even when inference demand was low, wasting thousands of dollars daily.
Solution
LLMOps Pipeline: Built an automated CI/CD pipeline for Python-based LangChain applications to ensure rapid, tested deployments.
Semantic Cache Implementation: Developed a Redis-based caching layer to serve common AI queries instantly, reducing LLM API costs.
Vector Index Optimization: Automated the re-indexing and metadata filtering of vector databases to ensure high-precision retrieval.
Serverless Inference: Implemented auto-scaling GPU clusters that spin down to zero during idle periods using Kubernetes Event-Driven Autoscaling (KEDA).
Tech Stack
AI Framework: LangChain & Python.
Intelligence: Google Gemini / OpenAI (Model Integration).
Database: Pinecone (Vector DB) & Redis (Caching).
DevOps: GitHub Actions & Kubernetes (KEDA).
Results
Latency Reduction: Improved user response times by 65% (from 10 seconds to ~3 seconds).
Cost Savings: Cut AI API and GPU compute costs by 40% via semantic caching and auto-scaling.
Accuracy Boost: Increased RAG retrieval accuracy by 25% through optimized vector indexing.
Dev Velocity: Allowed the AI team to deploy and test new prompt strategies 5x faster than before.