I build production-grade AI systems for complex regulated environments — where correctness, reliability, security, and explainability matter more than benchmarks.

My work sits at the intersection of:

• 📄 Document Intelligence (OCR + layout + page understanding + field extraction)
• 🧠 Multimodal AI (LLMs / SLMs / VLMs) for real healthcare workflows
• ⚙️ AI Platform Engineering (on-prem, cloud, gov cloud; microservices; CI/CD)
• ⚡ GPU + inference systems (CUDA, NVIDIA drivers, custom builds, profiling)

I’m passionate about Responsible AI in healthcare — making care faster and easier while staying audit-friendly, verifiable, and compliant.

A production platform that processes large medical claim PDFs end-to-end:

• OCR + document splitting
• Page classification (DistilBERT + rules) to categorize page types
• Layout detection (YOLO) to locate page regions (titles, summary boxes, checkboxes, etc.)
• Region cropping to improve OCR/VLM accuracy and reduce compute
• Field extraction (beneficiary details, NPI/MBI, HCPCS/CPT, ICD codes, claim lines, etc.)
• Decision support system using RAG, answering nurse review questions with evidence + traceability
• Human-in-the-loop UI: nurses can verify exactly where an answer came from

• Built a full end-to-end service operations module (frontend + backend) rapidly using AI-assisted development (Cursor/Claude)
• Implemented an OCR service using Azure Document Intelligence with custom-trained models for prior auth cover sheets
• Deployed services using Azure Web Apps

✅ Production Document Intelligence

• OCR pipelines, layout detection, page classification, field extraction
• Handling messy documents: handwriting, checkboxes, signatures, diagrams

✅ LLM/SLM/VLM Systems

• Model selection & benchmarking (LLaMA, Mistral, Phi-3, BioBERT, BioMedBERT, Bio-Mistral)
• RAG with citations + evidence grounding
• Guardrails using LangChain (safe inputs/outputs for regulated environments)
• Fine-tuning + CPU-first optimization via 4-bit quantization (C++/BitNet-style)

✅ GPU + Inference Engineering

• Bare-metal GPU bring-up (drivers, CUDA toolkit, NVCC, kernels)
• H100 / L40s / A40 / RTX 4090 / RTX 5090 / DGX environments
• Inference runtimes: PyTorch, ONNX Runtime, llama.cpp
• Custom builds aligned to CUDA versions + GPU compute capability

✅ Platform Engineering & Deployment

• Microservices architecture
• On-prem deployment on RHEL-based systems
• RPM packaging + systemd services for “install → services up”
• Containers + Kubernetes + ACR
• CI/CD (Azure DevOps) for RPMs + artifacts + deployments
• Experience across on-prem + cloud + gov cloud

✅ Reliability, Observability, Security

• Performance monitoring stack: Telegraf + InfluxDB + Chronograf
• System/service/GPU metrics and bottleneck analysis
• Production tuning: Uvicorn workers, CPU/I/O concurrency, DB pooling
• Security: HTTPS everywhere, cert-based auth, mTLS between services + to DB

✅ Leadership

• Led 15 interns (Label Studio + YOLO layout detection pipeline)
• Mentored 3 associates (install/test/validation, architecture onboarding)
• Acted as product-owner proxy / client-facing technical lead when needed

• Medium: https://medium.com/@abhishekgoud1212
• Portfolio: https://abhi0323.github.io/Abhishek-Chandragiri-Portfolio/

If you're building:

• AI platforms in regulated environments
• Document intelligence at scale
• Evidence-grounded RAG systems
• GPU-backed inference services

Let’s connect: https://www.linkedin.com/in/abhishek-chandragiri/