Building intelligent systems
that reason, retrieve,
and act.
I design and deploy production AI systems using LLMs, agentic workflows, retrieval-augmented generation, semantic search, and scalable AI infrastructure.
I build AI systems beyond simple LLM wrappers.
I build AI systems beyond standalone model calls. My work spans orchestration, retrieval, memory, tool use, knowledge systems, APIs, evaluation, and deployment.
I focus on turning LLM capabilities into production workflows by treating retrieval quality, system state, deployment, and reliability as first-class engineering problems.
Agentic Systems
Multi-agent orchestration with LangGraph, stateful workflows, and tool-using agents.
RAG & Retrieval
Vector and graph retrieval with FAISS, ChromaDB, Pinecone, and Neo4j for grounded responses.
LLM Engineering
Prompt engineering and model workflows across GPT-4, LLaMA2, and T5 for domain-specific automation.
Production AI / MLOps
Shipping AI services with Python APIs, Docker, Kubernetes, AWS, and CI/CD workflows.
Faster service response
Service response
Higher ingestion throughput
Data ingestion
Lower inference latency
Inference
Reduced manual review / triage
Claims review
Featured AI systems
Two production-focused case studies. The diagrams are representative architectures based on the technologies and system components used, rather than literal infrastructure maps. Hover a node for context.
Enterprise Multi-Agent RAG System
Healthcare claims review required substantial manual effort, with fraud evidence distributed across documents, metadata, and enterprise knowledge sources.
A representative architecture based on the technologies and system components used: LangGraph coordinates retrieval and reasoning workflows, vector stores provide grounded context, and GPT-4 / LLaMA2 process retrieved evidence for claims decision support.
Reduction in manual review time
Higher fraud detection accuracy
Animated to illustrate a request progressing through the major system stages.
Production Agentic AI Assistant
Enterprise workflows needed an assistant that could preserve context, use tools, retrieve grounded knowledge, and support multi-step interactions instead of relying on isolated model calls.
A representative architecture based on my agentic AI work: LangGraph manages workflow state, memory preserves context, ReAct-style tool use handles actions, and vector / graph retrieval supplies relevant enterprise knowledge before the final reasoning step.
Fewer SLA violations
Animated to illustrate a request progressing through the major system stages.
Career timeline
A curated view of the work most relevant to Generative AI, RAG, agentic systems, and production ML.
Responsive
Jul 2024 — PresentAI Engineer · Remote
- Designed LangGraph-powered GPT-4 chatbots for internal automation, improving service response time by 35% through multi-agent orchestration.
- Built memory-aware, tool-using agents with the LangChain React Agent framework for multi-step reasoning workflows.
- Engineered RAG pipelines with FAISS, ChromaDB, and Pinecone, increasing ingestion throughput 40% through token-optimized chunking and extending retrieval with Neo4j graph-based Q&A workflows.
Wipro
Dec 2021 — Mar 2024GenAI / ML Engineer · Hyderabad, India
- Designed RAG systems integrating GPT-4, LLaMA2, and T5 for healthcare claims automation and document summarization.
- Reduced manual triage by 35% through optimized RAG workflows and metadata retrieval for fraud detection.
- Reduced inference latency 20% through deployment optimization on AWS Lambda, EC2, and S3.
Zensar Technologies
Dec 2020 — Nov 2021Associate AI Engineer · Hyderabad, India
- Deployed transformer-based NER and classification models on AWS and Azure for finance and telecom.
- Built automated ETL pipelines with Airflow and Spark for large-scale structured and unstructured data.
Technical stack
Agentic AI
LLM & RAG
Retrieval & Knowledge
AI / ML
Production AI
Education
M.S. in Computer Science
Sacred Heart University
Fairfield, Connecticut
B.Tech in Electronics and Communication Engineering
Lakireddy Bali Reddy College of Engineering
India
Certifications
Generative AI LLMs
NVIDIA-Certified Associate
Generative AI with LLMs
DeepLearning.AI (Coursera)
2025
Artificial Intelligence A-Z
Udemy
2025
Certified Machine Learning Engineer
Udemy
2024
Let's build intelligent systems.
I'm interested in opportunities involving Generative AI, Agentic AI, RAG, LLM infrastructure, and production AI systems.