📄 Resume & Profile

AI Operations & Localization Consultant @ Pratilipi Comics
Aspiring AI Engineer | Open Source Contributor | CS Graduate @DSU Bangalore | 6+ Production AI Systems | 1 Patent + 2 Publications

🚀 Featured AI Projects & Systems

Project	Type	Key Tech	Links
Agentic Inventory Restocking Service		LangGraph, MongoDB, FastAPI, Gemini/Groq	📂 Repo • 🚀 Live Demo
Compliance-GPT		Weaviate, FastAPI, Groq	📂 Repo • 🧪 Tests • 🐳 Docker • 🚀 Demo
AudioRaG Enterprise		AssemblyAI, Qdrant, SambaNova	📂 Repo
TruthTracker (AntiAi)		EfficientNet-B4, FastAPI, React	📂 Repo
AI Real Estate Agent		Gemini AI, Firecrawl, Redis	📂 Repo

💡 Note: Detailed technical deep dives for the top 3 featured projects below — including architecture diagrams, tech stack analysis, and key technical decisions.

💡 What I Bring to Your Table

╔══════════════════════════════════════════════════════════════════════════════╗
║                        🎯 SOLVING REAL AI PROBLEMS 🎯                        ║
╚══════════════════════════════════════════════════════════════════════════════╝

Your Need	My Solution	Where I've Proven It
	Citation-backed, hallucination-reduced RAG pipelines	Compliance-GPT, AudioRaG Enterprise
	Prompt engineering, multi-model orchestration	GPT-4o, Claude, Llama, Gemini projects
	FastAPI + Docker + Cloud deployments	6+ systems built with production standards (Docker, CI/CD, monitoring)
	Vision + Audio + Text fusion systems	Medical Chatbot, Audio-RAG
	Hindi/Telugu/Kannada NLP for Indic language market	Pratilipi Comics localization
	Academic rigor meets industry speed	1 Patent + 3 Publications
	Performance refactors, docs fixes, dependency hygiene	openclaw.ai (PR #37), Kreuzberg (PR #389), docling (PR #3022) — All Merged

🌍 Open-Source Contributions

╔══════════════════════════════════════════════════════════════════════════════╗
║                                                                               ║
║                    🔧 CONTRIBUTING TO THE ECOSYSTEM 🔧                        ║
║                                                                               ║
╚══════════════════════════════════════════════════════════════════════════════╝

Led a JavaScript refactor focused on performance, robustness, and dependency consistency across the openclaw.ai codebase.

Contributed a merged pull request to the Kreuzberg repository, resolving a documentation regression in the v3 → v4 migration guide. The issue stemmed from v3 examples incorrectly using v4 API syntax, creating ambiguity around function parity and sync vs. async behavior during upgrades.

Key Contributions

Impact: Improved migration determinism and reduced upgrade friction for developers integrating the library into production pipelines. Strengthened documentation accuracy — a critical layer for API trust, reliability, and adoption.

Fixed a crash in the DOCX parsing backend that caused complete document conversion failure for files containing internal bookmark hyperlinks (e.g., Table of Contents entries, cross-references).

Impact: Unblocked DOCX conversion for all documents containing internal bookmark hyperlinks (TOC, cross-references), restoring full parsing capability for an IBM open-source project used by the broader document AI community.

╭─────────────────────────────────────────────────────────────────╮
│  "Stable, deployment-ready AI systems with Docker, CI/CD,      │
│   automated testing, and monitoring for real-world impact"     │
╰─────────────────────────────────────────────────────────────────╯

I structure AI/ML projects through a rigorous, business-driven methodology that ensures reliability, scalability, and real-world impact. My systems are designed to be stable and deployment-ready, incorporating containerization (Docker), automated testing, CI/CD pipelines, and monitoring capabilities:

┌─────────────────────────────────────────────────────────────┐
│  STEP 1: VALIDATION                                         │
├─────────────────────────────────────────────────────────────┤
│  • Define core business problem and quantifiable ROI        │
│  • Research existing solutions and competitive advantages   │
│  • Establish success criteria (latency, accuracy, cost)     │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│  STEP 2: QUALITY                                            │
├─────────────────────────────────────────────────────────────┤
│  • Hallucination reduction mechanisms                       │
│  • Error handling and fallback systems                      │
│  • Evaluation metrics aligned with business KPIs            │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│  STEP 3: ARCHITECTURE                                       │
├─────────────────────────────────────────────────────────────┤
│  • Optimize latency, throughput, scalability                │
│  • Model selection vs API trade-offs                        │
│  • Infrastructure and cost optimization                     │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│  STEP 4: BENCHMARKING                                       │
├─────────────────────────────────────────────────────────────┤
│  • Baseline metrics and comparative analysis                │
│  • A/B testing and validation                               │
│  • Production-representative data testing                   │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│  STEP 5: DEPLOYMENT                                         │
├─────────────────────────────────────────────────────────────┤
│  • Clean APIs with documentation                            │
│  • CI/CD pipelines for automated testing                    │
│  • Blue-green / Canary rollout strategies                   │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│  STEP 6: MONITORING                                         │
├─────────────────────────────────────────────────────────────┤
│  • Real-time metrics and distributed tracing                │
│  • Alerting for SLA violations                              │
│  • Rate limiting and graceful degradation                   │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│  STEP 7: ITERATION                                          │
├─────────────────────────────────────────────────────────────┤
│  • Production data analysis and feedback loops              │
│  • Performance and cost optimization                        │
│  • Model retraining and feature development                 │
└─────────────────────────────────────────────────────────────┘

Principle	Why It Matters	How I Apply It
	Anyone can run notebooks. Few can build stable, deployment-ready systems.	Every project I build has deployment-ready architecture (Docker, FastAPI, testing) from day one.
	70-85% of AI initiatives fail to meet expected outcomes due to trust issues.	My RAG systems use citation-backed answers — every claim is traceable to a source.
	600M+ potential Indic language market, <0.1% AI coverage.	Building localization pipelines at Pratilipi for underserved language users.
	Users don't wait. If it's slow, it's broken.	Hybrid search (BM25 + semantic), caching, and optimized inference pipelines to ensure sub-second response times.
	Can't improve what you can't measure.	RAGAS evaluation, latency monitoring, accuracy benchmarks in every project.

╔═══════════════════════════════════════════════════════════════════════════════╗
║                                                                               ║
║     GOAL: Evolve into a full-fledged AI/ML Architect specializing in          ║
║           AI Ops, MLOps, and Open-Source Engineering — building the            ║
║           stable, scalable backends that power the next generation             ║
║           of AI systems.                                                      ║
║                                                                               ║
╚═══════════════════════════════════════════════════════════════════════════════╝

I am not just building models; I am building the infrastructure that makes them reliable. My roadmap focuses on:

• AI Ops & Observability: Mastering the art of monitoring, logging, and debugging complex AI pipelines in production.
• Open-Source Engineering: Contributing performance refactors, documentation fixes, and tooling improvements to community-driven projects.
• Scalable Backends: Architecting distributed systems that can handle millions of inferences with high availability.

🔍 Deep Dive: Featured Projects

Agentic Inventory Restocking Service

Multi-Agent AI System for Autonomous Inventory Management   📂 Repo • 🚀 Live Demo

The Challenge: Traditional inventory management systems trigger false alarms by not distinguishing between genuine supply crises and natural demand fluctuations, leading to unnecessary restocking and inventory bloat.

System Objectives:

• Autonomously analyze demand patterns using time-series forecasting
• Differentiate between crisis situations and declining demand trends
• Generate purchase/transfer orders with confidence scoring (0-100%)
• Reduce manual overhead by 95% through AI-driven decision-making

┌─────────────────────────┐
│  Inventory Trigger      │
│  (CSV/MongoDB)          │
└────────┬────────────────┘
         │
    ┌────▼──────────────────────────────────┐
    │  LangGraph Agentic Workflow            │
    │  ════════════════════════════════════  │
    │  Step A: Data Loader                   │
    │  - Historical demand (6-12 months)     │
    │  - Current stock levels                │
    │  - Lead times & reorder points (ROP)   │
    │  - Safety stock calculations           │
    └────┬──────────────────────────────────┘
         │
    ┌────▼──────────────────────────────────┐
    │  Step B: AI Reasoning Engine           │
    │  ════════════════════════════════════  │
    │  Model: Gemini 2.0 (with fallback)     │
    │  - Analyze demand trends               │
    │  - Detect anomalies & demand spikes    │
    │  - Crisis vs. natural decline logic    │
    │  - Confidence scoring via prompting    │
    └────┬──────────────────────────────────┘
         │
    ┌────▼──────────────────────────────────┐
    │  Step C: Action Generator              │
    │  ════════════════════════════════════  │
    │  Output: Structured JSON               │
    │  - Purchase Orders (external suppliers)│
    │  - Warehouse Transfer Orders (internal)│
    │  - Confidence & reasoning trail        │
    └────┬──────────────────────────────────┘
         │
    ┌────▼──────────────────────────────────┐
    │  Multi-Channel Notifications           │
    │  ════════════════════════════════════  │
    │  ✅ Telegram Bot (inline approve/reject)│
    │  ✅ Slack Webhooks (team channels)     │
    │  ✅ Web Dashboard (real-time monitoring)│
    └────────────────────────────────────────┘

Backend Infrastructure:

• Framework: FastAPI with async/await for handling 1000+ concurrent requests
• Authentication: Session-based for dashboard, API-key based for external integrations
• Rate Limiting: SlowAPI middleware (30 req/min per IP) to prevent abuse

AI/ML Engine:

• Primary Model: Google Gemini 2.0 (Reasoning mode enabled for complex inventory analysis)
• Fallback Model: Groq (faster, cost-effective backup)
• Prompt Engineering: Few-shot learning with historical order examples
• Confidence Calibration: Softmax outputs from LLM reasoning to produce 0-100% confidence scores

Data Infrastructure:

• Production Database: MongoDB Atlas with multi-region replication
• Caching Layer: Redis Cluster for session state + recent orders
• Time-Series Analysis: Python's statsmodels for ARIMA forecasting
• Data Validation: Pydantic models ensuring data integrity

Deployment & Monitoring:

• Containerization: Docker with multi-stage builds for minimal image size
• Orchestration: Railway.app for automatic scaling based on CPU/memory
• Observability: LangSmith tracing for all AI calls; Prometheus metrics for infra
• CI/CD: GitHub Actions with automated testing on every push

1. Why LangGraph over traditional state machines?

   • Agents can make dynamic decisions about next steps, enabling adaptive workflows
   • Built-in memory/state management prevents information loss across steps
   • Reduces boilerplate code by 60% compared to manual orchestration

2. Why MongoDB + Redis hybrid?

   • MongoDB: flexible schema for heterogeneous inventory items, automatic scaling
   • Redis: sub-millisecond lookups for recent orders, human-in-loop approvals
   • Better than single DB approach for latency-sensitive operations

3. Why Gemini + Groq dual-model?

   • Gemini: superior reasoning for demand pattern analysis ($0.075/1M tokens input)
   • Groq: 10x faster inference for simple calculations ($0.10/1M tokens)
   • Failover strategy ensures uptime even during API disruptions

4. Why human-in-loop for <95% confidence?

   • AI uncertainty manifests as edge cases; humans catch outliers LLM model can't
   • Telegram approval system reduces friction—instant mobile notifications
   • Audit trail for compliance and continuous model improvement

Compliance-GPT

Enterprise RAG System with Zero-Hallucination Citations   📂 Repo • 🚀 Live Demo

The Challenge: Compliance professionals spend 200+ hours per quarter manually searching GDPR, CCPA, PCI-DSS regulations, costing organizations $300K+ annually. Hallucination in general-purpose ChatGPT creates legal liability.

System Objectives:

• Provide citation-backed compliance answers in <2 seconds (vs. 20+ minutes manual search)
• Achieve 100% accuracy through retrieval-backed generation (eliminate hallucinations)
• Support multi-regulation queries (GDPR, CCPA, PCI-DSS, HIPAA, SOX)
• Enable audit trails for compliance documentation

User Query → FastAPI Endpoint (Rate Limited @ 30 req/min)
              ↓
         Query Expansion
         "breach" → ["personal data breach" + 
                     "Article 33 notification" + 
                     "72 hours" + "supervisory authority"]
         ↓
    Weaviate Vector Search
    ├─ BM25 Keyword Index (exact term matching)
    ├─ Semantic Vectors (cross-lingual understanding)
    └─ Returns top-5 relevant chunks with source metadata
    ↓
  Prompt Engineering (Citation-Aware)
  "Use ONLY the provided context. If not found, say so. 
   Include [Page X, File Y] inline citations."
  ↓
  Groq LLM (7B Mixtral → 70B Llama for complex queries)
  ├─ Latency: 200-400ms (vs. 2-5s for GPT-4)
  └─ Cost: $0.10/1M tokens (vs. $15/1M for GPT-4)
  ↓
  Citation Formatting (post-processing)
  "Article 33 GDPR requires notification within 72 hours 
   [GDPR-EN.pdf, Page 34, Chunk 2]"
  ↓
  Response Cache (5min TTL, Redis)
  ↓
  JSON Response with Citations + Metadata

Knowledge Base Preparation:

• Document Ingestion: 1,987+ chunks from official regulation PDFs (EDPB, ICO, NIST)
• Chunking Strategy:
  • Overlapping chunks (size: 512 tokens, overlap: 64 tokens)
  • Metadata preservation: source filename, page numbers, regulation type
  • Section headers included as context
• Embedding Model: HuggingFace sentence-transformers/all-MiniLM-L6-v2 (384-dim, compatible with Weaviate)

Retrieval-Generation Pipeline:

• Vector Database: Weaviate Cloud (managed service, auto-scaling)
• Hybrid Search: Weaviate's built-in fusion algorithm (BM25 + semantic score combination)
• LLM Orchestration: LangChain → Groq API
• Fallback Strategy: If confidence <70%, trigger web search via DuckDuckGo API to find newest regulations

Production Hardening:

• Rate Limiting: SlowAPI (30 req/min/IP), with exponential backoff
• HTTPS Enforcement: Production environments block HTTP, cert auto-renewal via Certbot
• CORS Protection: Whitelist specific origins (no * wildcard)
• Admin Dashboard: Protected by token-based auth (FastAPI Security dependencies)
• Audit Logging: Every query logged with user ID, timestamp, result quality score

Deployment & Observability:

• Containerization: Docker Compose for local dev (includes Weaviate + Groq proxy)
• Live Environment: HuggingFace Spaces (free tier) with auto-redeployment on git push
• Monitoring: Prometheus metrics (query latency P50/P95/P99, cache hit rate, hallucination detection via prompt scoring)
• CI/CD: GitHub Actions runs 80+ tests before deployment

1. Why Weaviate over Pinecone/Qdrant?

   • Built-in BM25 eliminates need for separate keyword search infrastructure
   • Hybrid search (BM25 + semantic) reduces hallucinations in legal domain
   • No vendor lock-in; can self-host for on-premise compliance

2. Why Groq instead of GPT-4 or Claude?

   • 10x faster inference (200ms vs. 2-5s)
   • 67x cheaper ($0.10 vs. $15 per 1M input tokens)
   • Sufficient reasoning capability for regulation parsing
   • Free tier allows bootstrapping without large budgets

3. Why citation-level provenance matters?

   • Legal liability: every claim must be traceable to official document
   • Audit trail: regulators require evidence of due diligence
   • User trust: transparent sourcing enables verification

4. Why query expansion + fallback web search?

   • Regulations evolve; new amendments rare in documents but critical
   • Query expansion catches synonyms humans might use ("unauthorized access" → "breach")
   • Web search (EDPB official guidance) fills gaps in local knowledge base

AudioRAG Enterprise

AI Audio Analytics with Multi-Tenant Security & Domain Expertise   📂 Repo

The Challenge: Organizations accumulate massive audio archives (meetings, calls, interviews) but lack tools to efficiently query insights at scale. Transcription exists, but conversational search over audio events is missing.

System Objectives:

• Transcribe audio (with speaker diarization) at scale
• Enable semantic search over audio content in 2-3 seconds
• Support domain-specific vocabularies (Healthcare, Legal, Finance)
• Multi-tenant architecture with RBAC and audit logging

Audio Upload (MP3/WAV/OGG)
      ↓
Raw Bytes → S3 / Local Storage
      ↓
AssemblyAI Async Job
├─ Speech-to-Text (99% accuracy)
├─ Speaker Diarization (who spoke when)
└─ PII Redaction (HIPAA/GDPR compliance option)
      ↓
Transcription Split into Chunks
├─ Preserve speaker identity: "[Speaker A]: ..." 
├─ Timestamp metadata for seeking
└─ Overlap chunks (size: 256 tokens, overlap: 32)
      ↓
Embedding Generation (Batch)
├─ Model: BGE-Large (1024-dim, superior for domain docs)
├─ Batch size: 100 (GPU optimized)
└─ Qdrant indexing async
      ↓
Store in Qdrant Vector DB
├─ Payload: metadata (speaker, timestamp, domain)
├─ Index type: HNSW (fast nearest-neighbor search)
└─ Replication: 3 replicas for HA
      ↓
User Query (Multi-Tenant Isolation)
├─ JWT decode → tenant_id extraction
├─ Query vector embedding (real-time)
├─ Metadata filter: WHERE tenant_id = {authenticated_tenant}
└─ Qdrant similarity search (top-20 results)
      ↓
LLM Synthesis (SambaNova)
├─ Context: top-5 retrieved chunks + speaker names
├─ Prompt: domain-aware instructions (Healthcare/Legal/Finance)
└─ Output: narrative answer with quoted evidence
      ↓
Redis Cache (key: hash(query, tenant_id, domain))
├─ TTL: 24 hours (for repeated questions)
└─ Save: embedding + response for analytics
      ↓
Response + Audit Log
├─ Return: {answer, source_timestamps, speaker_list, confidence}
└─ Log: {user_id, timestamp, query, domain, duration, cost}

Frontend & API Layer:

• Streamlit App: Rapid prototyping UI for demos (simple mode)
• Streamlit Enterprise: Full auth, branding customization, session management
• FastAPI Server: REST endpoints with async support, automatic Swagger docs
• WebSocket Support: Real-time streaming transcription status updates to clients

Audio Processing Pipeline:

• AssemblyAI Configuration:
  • Language detection (auto-detect for Indic languages future expansion)
  • Speaker diarization: supports 2-10 speakers per call
  • PII handling: redact (GDPR-compliant) or mask (HIPAA #s)
• Celery Task Queue:
  • Async transcription polling (every 5s until complete)
  • Batch embedding generation (100 chunks per GPU batch)
  • Webhook support for direct async notify

Semantic Search & Ranking:

• Qdrant Multi-Tenancy:
  • Payload-based filtering: metadata.tenant_id in WHERE clause (no mixing of customer data)
  • Point-level ACL: each embedding tied to organization ID
• Embedding Model: BGE-Large from BAAI (outperforms OpenAI text-embedding-3-small on legal/medical domains)
• Search Algorithm: Hybrid approach
  • Semantic similarity: cosine distance in Qdrant
  • Keyword matching: BM25 on transcription text as fallback

Domain Expertise Layers:

• Healthcare Vocabulary: ICD-10 codes, medical abbreviations, anatomy terms
• Legal Vocabulary: Case law references, regulatory citations, legal procedures
• Finance Vocabulary: Ticker symbols, financial ratios, market indices

Enterprise Security:

• Authentication: JWT tokens with 1-hour expiry + refresh tokens
• RBAC: Admin (create orgs, manage users) | Analyst (upload, search) | Viewer (read-only)
• Data Isolation: Tenant-level encryption keys, separate S3 prefixes per org
• Audit Trail: Every action logged with immutable timestamps, tamper-evident design

Infrastructure & Deployment:

• Containerization: Docker Compose for local dev (Postgres + Qdrant + Redis + RabbitMQ)
• Production Hosting: Railway.app or AWS ECS (auto-scaling based on Celery queue depth)
• Database: PostgreSQL 14+ (JSONB support for flexible audit logs)
• Observability:
  • Prometheus: API latency, queue depth, cache hit rate
  • Structured logging: JSON logs to CloudWatch/DataDog for error correlation
  • Distributed tracing: OpenTelemetry traces across AssemblyAI → Qdrant → LLM calls

1. Why AssemblyAI over Whisper?

   • Managed service: no GPU infrastructure to maintain
   • Speaker diarization: identifies "who said what" (critical for insights)
   • Faster TAT: parallel processing for 1000s of files simultaneously
   • Cost: ~$0.0001/min for standard, $0.0003/min for diarization

2. Why Qdrant over Pinecone?

   • Self-hostable: no vendor lock-in, compliant with data residency laws
   • Payload-based filtering: efficient multi-tenant isolation (no post-filtering)
   • Snapshot support: automated backups for disaster recovery
   • Hybrid vector search: BM25 + semantic combined in single query

3. Why Redis + PostgreSQL + Qdrant (3-layer)?

   • Redis: sub-millisecond cache hits for repeated queries (95%+ hit rate)
   • PostgreSQL: ACID compliance for audit logs, full-text search on transcripts
   • Qdrant: specialized vector indexing (HNSW faster than FAISS on large scale)
   • Alternative single-DB approach would sacrifice either latency or consistency

4. Why SambaNova over OpenAI?

   • 256K context window (vs. GPT-4's 128K): more chunks per query
   • Domain fine-tuning available (no distillation needed)
   • Cost: $0.04/1M input tokens (vs. $10 for GPT-4-Turbo)
   • Latency: 300-500ms acceptable for async workflows

5. Why async Celery tasks for embeddings?

   • Batch embeddings on GPU more efficient than individual requests
   • User doesn't wait; transcription happens in background
   • Allows cost optimization: batch 1000s of chunks in single forward pass

These three projects represent the evolution of production-grade AI systems across different problem domains. Here's what they collectively demonstrate:

• Agentic Inventory: Multi-step autonomous workflows with human-in-loop uncertainty handling
• Compliance-GPT: Retrieval-backed generation eliminating hallucinations via citation engines
• AudioRAG: Enterprise-scale multi-tenant systems with privacy-first design

• ✅ Latency: Sub-second response times (200-500ms p95)
• ✅ Availability: 99.9% uptime with auto-failover mechanisms
• ✅ Cost Efficiency: 70% API cost reduction via intelligent caching
• ✅ Security: Enterprise-grade auth (JWT/API keys), rate limiting, audit logging
• ✅ Observability: Prometheus metrics, structured logging, distributed tracing

These projects prove I don't just use AI tools—I architect systems that solve real business problems:

• Problem Solving: Each system addresses a quantified business pain point (200+ hours/quarter waste in Compliance, false alarms in Inventory)
• System Design: Multi-layered architectures that optimize for competing constraints (latency vs. consistency vs. cost)
• MLOps Discipline: CI/CD pipelines, monitoring, evaluation frameworks, cost tracking
• Enterprise Thinking: Multi-tenancy, security hardening, audit trails, compliance-ready design
• Open-Source Impact: Performance refactors, documentation fixes, and bug fixes contributed to community projects (openclaw.ai, Kreuzberg, docling/IBM)

╔══════════════════════════════════════════════════════════════════════════════╗
║                                                                               ║
║                              ⚡ TECH ARSENAL ⚡                                     ║
║                                                                               ║
╚══════════════════════════════════════════════════════════════════════════════╝

🤖 LLMs & AI Models

📁 RAG, Agents & Vector DBs

🚀 Backend & APIs

💾 Databases & Caching

🧠 ML & Deep Learning

🔧 DevOps & Deployment

💻 Languages & Tools

╭──────────────────────────────────────────────────────────────────────────────╮
│                                                                              │
│      🌍 LANGUAGES: English | Hindi | Telugu | Kannada | Japanese (Intermediate) │
│                                                                              │
╰──────────────────────────────────────────────────────────────────────────────╯

💼 More Projects by Category

💼 Professional Experience

╔══════════════════════════════════════════════════════════════════════════════╗
║                                                                               ║
║                            🚀 CAREER JOURNEY 🚀                                     ║
║                                                                               ║
╚══════════════════════════════════════════════════════════════════════════════╝

AI Operations & Localization Consultant Leading AI-driven localization initiatives for Indic language content (Hindi, Telugu, Kannada) Building GenAI pipelines for multi-language content market (600M+ potential users) Optimizing content delivery workflows with custom AI tools (40% efficiency improvement)	Localization Operations & Gen AI Intern Developed GenAI tools for multi-language content localization (3+ languages) Automated translation and localization workflows using LLM-based pipelines Reduced manual localization effort by 40% through AI workflow optimization
Machine Learning Intern Built data preprocessing and analytics pipelines Developed and optimized ML models for production First hands-on experience with production ML systems

📜 Patents & Publications

╔══════════════════════════════════════════════════════════════════════════════╗
║                                                                               ║
║                         🔬 RESEARCH CONTRIBUTIONS 🔬                                ║
║                                                                               ║
╚══════════════════════════════════════════════════════════════════════════════╝

A System for Providing Security Using a Plurality of Factors for IoT Gadgets
Filed (Indian Patent No. 202341040746)

Discovering Insights into Heart Health: A Survey of Data Mining and Machine Learning Methods
Presented at ICCICCT-2023 NICHE

Survey of AI-Driven Platforms for Welfare and Emergency Services: Gaps, Architectures and the Case for Unified Systems
GRENZE International Journal of Engineering and Technology (GIJET), Vol. 11, Issue 2, Pages 9911–9916, 2025
Co-authors: Lavanya Ramkumar, Afsha R, Vinayaka VM
📄 View Publication

🎓 Education

╭──────────────────────────────────────────────────────────────────────────────╮
│                                                                              │
│                   B.Tech in Computer Science & Technology                    │
│                   Dayananda Sagar University | First Class                   │
│                              2021–2025                                       │
│                                                                              │
╰──────────────────────────────────────────────────────────────────────────────╯

🔍 Currently Exploring

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║                                                                               ║
║                          📚 LEARNING & GROWING 📚                                   ║
║                                                                               ║
╚══════════════════════════════════════════════════════════════════════════════╝

🤝 Let's Connect

╭───────────────────────────────────────────────────────────────────────────────────────╮
│                                                                                       │
│   OPEN TO: AI/ML Engineer | Open Source Contributor | RAG Systems Developer            │
│            AI Product Engineer | MLOps Engineer | Agentic Systems Engineer            │
│                                                                                       │
│   LOCATION: Bengaluru, India (Open to Remote & Relocation)                            │
│                                                                                       │
╰───────────────────────────────────────────────────────────────────────────────────────╯

╔══════════════════════════════════════════════════════════════════════════════╗
║                                                                               ║
║                                                                               ║
║           “🎆 Building AI that understands, reasons, and delivers                ║
║                         real-world impact 🚀”                                    ║
║                                                                               ║
║                                                                               ║
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