Pavan Sekhar Mandavilli
Pavan Sekhar Mandavilli

Software Engineer (AI/ML)

Open to offers · Member since 3 Nov 2026
Location
Bengaluru, India
Desired salary
Unspecified
Work preference
Remote Only
Experience level
Mid

About

Professional summary

I am a Software Engineer specializing in AI/ML systems, with nearly two years of experience building production-grade agentic AI platforms, multi-tenant MCP servers, and enterprise RAG pipelines.

I currently work at Kapture CX, where I design FastAPI-based AI agent orchestration microservices for distributed enterprise workflows. My work emphasizes reliability, scalability, and low-latency execution in production environments.

I have hands-on experience with LangChain, LangGraph, Model Context Protocol (MCP), large language models, multi-agent orchestration, prompt engineering, and retrieval-augmented generation. I build stateful agent workflows with streaming responses, checkpointing, and serverless tool execution.

My machine learning background includes deep learning, NLP, computer vision, model fine-tuning, feature engineering, PyTorch, TensorFlow, Hugging Face Transformers, and Scikit-learn. I have also developed real-time computer vision solutions using YOLOv5, OpenCV, and TensorRT.

I am proficient in Python backend development and cloud-native infrastructure, including FastAPI, SQL, PostgreSQL, MongoDB, Redis, Celery, Kafka, Docker, GCP, OAuth2, JWT, and observability tools. I focus on delivering secure, fault-tolerant, measurable AI systems that improve retrieval quality, inference latency, and throughput.

Notice period: immediately

Skills

24 capabilities

Tech stack & tools

Working toolkit

Application Hosting

Development

Monitoring

Experience

Career history

Software Engineer Kapture CX

I design and deploy production-grade FastAPI AI-agent orchestration microservices for distributed enterprise workflows, maintaining 99.9% uptime. I build stateful LangGraph multi-agent pipelines with serverless graph execution, fault-tolerant state checkpointing, and real-time response streaming.

I integrate dynamic MCP tool registration with Google Cloud Functions to support concurrent enterprise tenants without downtime. I also architect RAG and hybrid-search document-processing pipelines, improving retrieval accuracy by 35%, and implement Redis-based semantic and inference caching that reduces API latency by 20% and increases throughput by 35%.

Education

Learning history

KL University

B.Tech, Artificial Intelligence and Data Science

Completed a Bachelor of Technology in Artificial Intelligence and Data Science with a CGPA of 9.3/10.0.

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