// Content data — updated from Rohan's May 2026 resume const profile = { name: "Rohan Waghmare", role: "Software Developer (AI)", location: "San Francisco Bay Area, CA", status: "open-to-opportunities", summary: "I build production GenAI systems — agentic pipelines, RAG architectures, LLM fine-tuning, and MLOps — on GCP, AWS, and SAP BTP. 1+ years shipping enterprise AI at scale with measurable impact. Open to relocation. Available immediately.", email: "rwaghmare@binghamton.edu", phone: "+1 (607) 245-6001", site: "rohanwaghmare.com", linkedin: "/in/rohanwaghmare", github: "/ron103", }; const experience = [ { id: "sap", company: "SAP", role: "Software Developer iXP (AI)", location: "Palo Alto, CA", start: "Jan 2026", end: "Present", period: "2026 —", tags: ["AI/ML", "MLOps", "Backend", "Cloud"], bullets: [ "Replaced a multi-hop GPT-4 pipeline at 10× lower cost by fine-tuning DeepSeek (QLoRA) on SAP BTP — 100% domain extraction accuracy at 8s inference. Stack: HuggingFace, ArgoCD, KServe, FastAPI.", "Cut developer query-to-doc time by 92% (1hr → 5min) for 5,000+ engineers: RAG over a SPARQL Knowledge Graph with confidence-scored retrieval, exposed as an MCP server (SSE, JSON-RPC 2.0) for Claude/Cline on SAP HANA.", "Engineered a multi-step agentic pipeline (LangGraph + GPT-4) on SAP BTP with SPARQL KG retrieval, backend update tool access via MCP, and enterprise-grade auth/auditing through PKCE + SAP IDP.", ], }, { id: "mihin", company: "Michigan Health Information Network", role: "Software Engineer", location: "Binghamton, NY (Remote)", start: "Mar 2025", end: "Jan 2026", period: "2025 — 2026", tags: ["Backend", "DevOps", "Cloud"], bullets: [ "Engineered a Python data pipeline processing 500K+ records across 10+ organizations — AWS ECS Fargate, S3, EventBridge, DynamoDB, Docker/Terraform, cross-month aggregation logic.", "Cut processing latency 40% by refactoring the S3 Select API with conditional ScanRange and multipart parallelization via ThreadPoolExecutor; 20MB chunking across multi-GB files.", "Built a Python + Selenium scraper to migrate NPI directory data at scale, eliminating 60% of manual processing.", ], }, ]; const projects = [ { id: "deepseek-pipeline", title: "DeepSeek Fine-Tuning Pipeline on SAP BTP", year: "2026", kind: "MLOps", tags: ["QLoRA", "DeepSeek", "KServe", "ArgoCD", "AWS"], blurb: "Swapped a multi-hop GPT-4 pipeline for a fine-tuned DeepSeek model with a full MLOps loop — 10× cheaper, 100% accurate.", metrics: [ ["100%", "domain extraction accuracy"], ["10×", "cost reduction vs GPT-4"], ["8s", "inference"], ], details: { problem: "The incumbent domain-extraction pipeline chained multiple GPT-4 calls — slow, expensive, hard to reproduce. Each hop added latency and cost.", approach: [ "Fine-tuned DeepSeek with QLoRA on curated SAP domain data, versioned in HuggingFace.", "Built the full MLOps loop: ArgoCD for GitOps, KServe for model serving, FastAPI for the inference gateway, AWS S3 for artifact storage.", "Deployed on SAP BTP alongside the existing pipeline so traffic could shift gradually with zero downtime.", ], stack: ["QLoRA", "DeepSeek", "HuggingFace", "KServe", "ArgoCD", "FastAPI", "AWS S3", "SAP BTP"], outcomes: [ "100% domain extraction accuracy on the SAP eval set.", "8 second end-to-end inference.", "10× lower cost than the GPT-4 chain it replaced.", ], }, }, { id: "mcp-rag-server", title: "RAG + MCP Server for SAP HANA", year: "2026", kind: "Developer Tooling", tags: ["MCP", "RAG", "SPARQL", "LangGraph", "SSE"], blurb: "Claude and Cline plug into confidence-scored RAG over SAP's SPARQL Knowledge Graph. Query-to-doc: 1hr → 5min for 5,000+ devs.", metrics: [ ["92%", "time reduction"], ["5,000+", "engineers served"], ], details: { problem: "SAP developers spent up to an hour stitching together SPARQL queries and documentation for every non-trivial question. The process was manual, error-prone, and blocked by auth.", approach: [ "Built a RAG pipeline over SAP's SPARQL Knowledge Graph with confidence-scored retrieval — low-confidence results trigger automatic query refinement.", "Exposed it as an MCP server with SSE endpoints and JSON-RPC 2.0 bidirectional streaming, making Claude and Cline first-class clients.", "Implemented enterprise-grade auth/auditing through PKCE + SAP IDP, so the server works inside SAP's security perimeter.", "LangGraph orchestrates multi-step agentic sequences when a single retrieval isn't enough.", ], stack: ["MCP", "SSE", "JSON-RPC 2.0", "SPARQL", "RAG", "SAP HANA", "LangGraph", "FastAPI", "PKCE"], outcomes: [ "Query-to-documentation time: 1hr → 5min (−92%).", "5,000+ SAP engineers using it in production.", "Zero extra API keys needed — auth flows through PKCE + SAP IDP.", ], }, }, { id: "layoff-tracker", title: "Industry-Specific Layoff Tracker", year: "2024", kind: "Data Pipeline", tags: ["Python", "Flask", "MongoDB", "NLP"], blurb: "Real-time ingestion of 208K+ Reddit & 4chan posts/month with sentiment + toxicity scoring and Plotly dashboards.", metrics: [ ["208K+", "posts / month"], ["98%", "sentiment accuracy"], ], details: { problem: "Layoff conversation is fragmented across forums. How do you surface early, industry-specific signals before they hit the news?", approach: [ "Built a Faktory-backed ingestion pipeline crawling Reddit + 4chan for posts matching industry + role vocabularies.", "Ran each post through NLTK + a fine-tuned classifier for sentiment and toxicity scoring, stored in MongoDB.", "Exposed Python/Flask REST APIs powering Plotly dashboards for trend visualization by time, company, and industry.", ], stack: ["Python", "Flask", "MongoDB", "Faktory", "NLTK", "Plotly", "REST"], outcomes: [ "98% sentiment/toxicity classification accuracy on labeled set.", "Ingests 208K+ posts/month at steady state.", ], }, }, { id: "tb-detection", title: "Tuberculosis Detection via Transfer Learning", year: "2023", kind: "Research / Medical AI", tags: ["TensorFlow", "ResNet-50", "Computer Vision", "IEEE"], blurb: "ResNet-50 transfer learning on TBX11K, validated with Grad-CAM / F1 / AUC. Co-authored an IEEE paper.", metrics: [ ["92%", "accuracy"], ["60+", "studies reviewed"], ], details: { problem: "Build a reproducible medical AI pipeline for chest X-ray TB detection, with interpretability as a first-class concern.", approach: [ "Trained ResNet-50 in TensorFlow on TBX11K with CLAHE preprocessing and augmentation.", "Validated predictions with Grad-CAM heatmaps, F1, and AUC — so clinicians can inspect why the model fired.", "Reviewed 60+ studies to situate the work; co-authored an IEEE-published paper on the pipeline.", ], stack: ["TensorFlow", "ResNet-50", "Python", "Grad-CAM", "CLAHE"], outcomes: [ "92% accuracy on TBX11K test split.", "Grad-CAM overlays line up with clinically salient regions.", "Published IEEE paper.", ], }, }, ]; const education = [ { school: "Binghamton University, SUNY", degree: "M.S. Computer Science", period: "Aug 2023 — Dec 2025", courses: "Algorithms · Operating Systems · Computer Networks · Design Patterns · Data Science Pipeline", }, { school: "School of Engineering, MIT ADT", degree: "B.Tech Computer Science", period: "Aug 2019 — May 2023", courses: "Databases · Machine Learning · Deep Learning · Operations Research · Big Data", }, ]; const skills = [ { group: "Languages", items: ["Python", "JavaScript", "TypeScript", "React", "Node", "Express", "C", "SQL"], }, { group: "AI / ML", items: ["LLM Fine-tuning", "QLoRA", "PEFT", "HuggingFace", "LangChain", "LangGraph", "PyTorch", "TensorFlow", "Scikit-learn", "RAG", "OpenCV"], }, { group: "Cloud & DevOps", items: ["GCP", "Vertex AI", "BigQuery", "Cloud Run", "AWS", "SAP BTP", "SAP AI Core", "Docker", "Kubernetes", "KServe", "Argo Workflows", "Terraform", "AWS CCP"], }, { group: "Backend & APIs", items: ["FastAPI", "Flask", "Django", "REST", "MCP", "SSE", "JSON-RPC 2.0", "SPARQL", "RDF"], }, { group: "Databases", items: ["PostgreSQL", "SAP HANA", "MongoDB", "MySQL", "Firebase", "SQLite"], }, { group: "Tools", items: ["Git", "Linux/Unix", "Postman", "Pandas", "NumPy", "Selenium", "Grafana", "Agile/SCRUM"], }, ]; window.__data = { profile, experience, projects, education, skills };