MODEL LAYER
Foundation reasoning & multimodal intelligence powering structured workflows.
GPT-4oClaude 3GeminiLlamaHuggingFace
Used in multi-agent orchestration, structured RAG, and enterprise automation.
1.5+
Years of hands-on experience
Tech Stack
Languages & Runtimes
FastAPIFlaskBackend Integration for AINo-Code / Low-Code Automation
Agent Orchestration
LangGraphCrewAICustom Multi-Agent SystemsEnd-to-End AI Pipelines
Vector DBs & RAG
ChromaDBPineconeWeaviateRAG Pipelines
Certifications
"The true logic of this world is in the calculus of probabilities."
— James Clerk Maxwell
Intelligence Architecture
API & INTEGRATION LAYER
Provider abstraction and multi-model switching via unified interfaces.
OpenAI APIAnthropic APIGemini API
Client → API Gateway → Model Provider
DATA & RETRIEVAL LAYER
Contextual grounding using vector search and structured retrieval pipelines.
ChromaDBPineconeWeaviateRAG PipelinesMongoDBRedisFirebase
User Query→Embed→Vector Search→Context Injection→Model
AGENT ORCHESTRATION
Stateful, multi-step reasoning systems with graph-based execution.
LangGraphCrewAICustom Multi-Agent SystemsEnd-to-End AI Pipelines
Planner→Tools→Memory→Executor→Output
BACKEND & SECURITY
Authenticated, access-controlled, production-grade APIs.
JWTOAuth 2.0API Access ControlREST APIsFull-Stack AI Applications
RBACRate LimitingSecure Token Handling
PERCEPTION & DEEP LEARNING
Vision pipelines and domain-specific model training.
Deep LearningPyMuPDF (PDF reading / thumbnails)Pillow (image processing)pypdf
CNNMultimodal ProcessingEdge Inference
DEPLOYMENT & MONITORING
Scalable deployment with logging, monitoring and CI/CD.
LoggingCI/CDAPI Monitoring
Latency · Error Rate · Throughput · Cost
CLOUD INFRASTRUCTURE
Elastic compute across cloud and serverless environments.
CI/CDAPI MonitoringAzureGoogle CloudVercelRender
Selected Projects
Prism – AI-Powered Recruitment Automation
Problem
Prism turns the fragmented recruitment process into a cohesive automation layer.
Solution
Built on n8n as the central orchestrator.
Impact
Eliminated 15-30m of manual work per candidate.; Standardized AI scoring criteria for every…
Click to view details and links
More projects
Credit Card Fraud Detection
ML
Constructed a fraud detection model using Logistic Regression, achieving 95.1% training and 94% testing accuracy, effectively reducing false positives. Leveraged NumPy, pandas, and scikit-learn for data cleaning, feature engineering, and seamless real-time fraud detection.
Tech
MLNumPypandasscikit-learn
Deep Dive Case Studies
Narratives of engineering journeys, from architectural decisions to deployment challenges.
Recent Blogs
View AllNo blog posts yet.