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
AI Context Engineer
Devulapelly Kushal Kumar
AI context engineer focused on production-grade agentic AI and enterprise automation. Designing scalable LLM-driven architectures that integrate backend engineering with intelligent orchestration. Turning AI concepts into deployable, secure, and business-aligned systems.
Tech Stack
Agent Orchestration
LangfuseCustom Multi-Agent SystemsPrompt EngineeringAgent-Based Automation
Vector DBs & RAG
ChromaDBRAG Pipelines
Storage
MongoDBRedis
"Precision in work. Integrity in decisions. Impact in everything."
— Devulapelly Kushal
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.
ChromaDBRAG PipelinesMongoDBRedis
User Query→Embed→Vector Search→Context Injection→Model
AGENT ORCHESTRATION
Stateful, multi-step reasoning systems with graph-based execution.
LangfuseCustom Multi-Agent SystemsPrompt EngineeringAgent-Based Automation
Planner→Tools→Memory→Executor→Output
BACKEND & SECURITY
Authenticated, access-controlled, production-grade APIs.
Node.jsNestJSReactREST ArchitectureRBACAPI Access ControlEnterprise AI Governance
RBACRate LimitingSecure Token Handling
PERCEPTION & DEEP LEARNING
Vision pipelines and domain-specific model training.
Document IntelligenceStructured Extraction
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.
AWS (EC2 / Lambda)Azure
Selected Projects
1 / 5
AgenticAI Data Labeling Platform
Problem
Data labeling is the bottleneck of modern AI.
Solution
The system uses a Hub-and-Spoke agent architecture.
Impact
Reduced TTM (Time to Market) by 4 months; Surpassed human-crowdsourced accuracy
Click to view details and links
More projects
AgriHelp Supply Chain Management
Flask
Full-stack AI app with Agentic AI and ML for seamless seed-to-product supply chain. Developed Flask backend and React frontend for real-time crop health monitoring and agricultural trend visualization. Built deep learning models for disease classification, yield prediction, and explainable AI insights.
Tech
FlaskReactTensorFlowPyTorchSHAP/LIMEAgentic AIML
Deep Dive Case Studies
Narratives of engineering journeys, from architectural decisions to deployment challenges.
Recent Blogs
View AllNo blog posts yet.