Hemanth Kumar Mangalapurapu
Architecting high-concurrency systems, enterprise AI pipelines, and distributed cloud infrastructure with high real-world impact.
Full-Stack & AI Engineer with 5 years of experience building Python/Go backends, React/Next.js frontends, and enterprise RAG & agent orchestration.
Technical Arsenal
About Me
I'm a builder at heart—someone who loves turning real-world problems into reliable, user-friendly software & comfortable owning complex software end-to-end—from high-throughput Python and Go backend microservices to dynamic React and Next.js user interfaces.
Currently at LeapGen AI, building enterprise RAG pipelines and multi-agent workflow orchestration using LangGraph and Model Context Protocol (MCP). Previously at SmartRevIQ (2023–25), engineered a revenue intelligence platform powered by schema-driven form engines and AI pricing agents.
Architected three flagship builds—an enterprise RAG knowledge base, a distributed Go container orchestrator, and a high-concurrency transit booking engine.
“I like systems where correctness under concurrency actually matters. Moving deeply into AI/LLM work, I care immensely about the boring infra—observability, idempotency, crash-safety—before trusting any optimization on top of it.”
Skill Distribution
M.S. Computer Science (May 2025)
Highlights
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HemGPT: Enterprise RAG System
A production-grade RAG platform enabling secure multi-document reasoning over 10,000+ internal documents via hybrid vector search, Reciprocal Rank Fusion, and local Ollama inference.
What it does
- •Engineered a hybrid retrieval engine pairing ChromaDB vector embeddings with BM25 keyword search, fused via Reciprocal Rank Fusion.
- •Implemented cross-encoder reranking and context compression to eliminate low-relevance passages before LLM context injection.
- •Integrated local Ollama inference for query expansion and token streaming with zero external API data leakage.
AuraDeploy: Distributed Container Orchestration Engine
A high-availability container orchestration engine written natively in Go, leveraging embedded Raft consensus, custom CNI networking, and CRI-O/containerd OCI runtimes.
What it does
- •Architected an Active-Passive high-availability control plane in Go using embedded HashiCorp Raft for distributed consensus, log replication, and FSM snapshots with zero external DB dependency.
- •Integrated native CRI-O / containerd runtime interfaces (`containerd/oci`) for staging OCI images, configuring cgroup limits, and managing network namespaces via `netlink`.
- •Designed a custom scheduling loop with predicate filtering (`HasSufficientResources`, `VolumeNodeAffinity`) and `LeastAllocated` priority scoring for optimal cluster workload placement.
SunX: AI Radiography & Clinical Decision Support
An AI radiography platform integrating a DenseNet-121 vision model for chest X-ray pathology detection with a pgvector RAG engine to surface evidence-based clinical treatment guidelines.
What it does
- •Built a DICOM image processing and HIPAA-aware PHI anonymization pipeline using `pydicom` and `MONAI` for automated medical image ETL.
- •Deployed a fine-tuned DenseNet-121 PyTorch vision model on NVIDIA Triton Inference Server to classify 14 pathology classes (Pneumonia, Cardiomegaly, Effusion, etc.).
- •Engineered a clinical RAG pipeline using LangChain and `pgvector` over 1536-dimensional embeddings, retrieving treatment recommendations grounded in AHA/ACC and IDSA medical guidelines.
Operational Workflow
Let's Connect
I am currently seeking full-time opportunities. If you have a role that fits my skills in Full Stack Dev or Data Analysis, let's chat.