# Vishvdeep Dashadiya (AI with VD) — Senior AI Engineer

> I design, build, and deploy intelligent products with rigorous engineering, clear metrics, and a calm delivery cadence. From agentic workflows to resilient MLOps, we move from prototype to scale without chaos.

Portfolio and technical blog. Core focus: Agentic AI, real-time ML systems, and cloud-native infrastructure. Areas of expertise: Machine Learning, Generative AI, LLM Engineering, Agentic Systems, MLOps, RAG, LangGraph, FastAPI.

## About

I design, build, and deploy intelligent products with rigorous engineering, clear metrics, and a calm delivery cadence. From agentic workflows to resilient MLOps, we move from prototype to scale without chaos.

- Website: https://aiwithvd.com
- Email: contact@aiwithvd.com
- LinkedIn: https://www.linkedin.com/in/vishvdeep/
- GitHub: https://github.com/aiwithvd

## Expertise

- Machine Learning: Production-grade ML systems built for measurable business impact.
- GenAI Engineering: LLM-powered products that scale from idea to deployment.
- MLOps: Operational excellence for models in the wild.

## Experience

- Senior Software Engineer - AI/ML @ BankBenchers (Nov 2025 - Present): Leading AI/ML initiatives with a focus on agentic systems, cloud-native deployment, and scalable production workflows.
- Board of Studies - External Industry Expert @ Atmiya University (May 2024 - Present): Advising academic curriculum and industry alignment for AI and data science programs, bridging classroom outcomes with real-world needs.
- Senior AI Engineer @ hypREspace (Jul 2025 - Oct 2025): Delivered AI consulting and platform capabilities, emphasizing AWS-based deployments and applied generative AI solutions.
- Senior Software Engineer @ 7Span (2024 - 2025): Spearheading GenAI and ML applications in Python, building scalable pipelines, and integrating AI solutions into enterprise systems.
- Data Science Engineer & Manager @ fxis.ai (2022 - 2024): Led ML engineering teams through delivery, focusing on data quality, model optimization, and stakeholder alignment.
- ML Consultant @ Self-Employed & mlearning.substack.com (2021 - 2022): Provided AI/ML strategy and built educational content on emerging technologies and practical adoption.
- Software Engineer @ Accenture (2019 - 2022): Drove feature development, impact analysis, and cross-team delivery in Agile environments.

## Projects

- [Fine-Tuned Tool-Calling Agent — A Small Model Taught to Use 40+ Tools](https://aiwithvd.com/projects/tool-agent-finetune): A LangGraph ReAct agent over a custom MCP backend exposing 40+ tools, with the decision model replaced by a fine-tuned Gemma 3 4B. Successful multi-turn tool trajectories from production were filtered and reformatted into an SFT dataset, then used to train Gemma 3 4B with QLoRA — cutting cost and latency while making tool-call formatting far more reliable.
- [Document Extraction Pipeline — Turn Documents into Structured Data with AI](https://aiwithvd.com/projects/document-extraction): A production-ready FastAPI service that extracts structured data from PDFs and images using OCR and LLM technology. Features async processing, JWT authentication, and support for multiple document types including invoices, legal documents, and ESG reports.
- [LangChain DeepAgent — Production-Ready AI Agent with Specialized Skills](https://aiwithvd.com/projects/deepagent): A FastAPI service powering a LangChain DeepAgent backed by Ollama llama3.2:3b. Features four specialized skills for complex reasoning tasks, SSE streaming for real-time responses, Redis rate limiting, and multi-turn session memory.
- [BoxCricket Umpier — AI-Powered Cricket Scoring for Gully Cricket](https://aiwithvd.com/projects/boxcricket): A Next.js 14 mobile-optimized cricket scoring application for box cricket and gully cricket matches. Features ball-by-ball scoring, intelligent rules engine, live statistics, and smart undo functionality.
- [Voice AI Demo — Self-Hosted Conversational Voice AI Assistant](https://aiwithvd.com/projects/voiceai): A fully self-hosted, open-source conversational voice AI assistant powered by LiveKit Agents. Features real-time WebRTC audio transport, a configurable AI pipeline with VAD, STT, LLM, and TTS, a polished Next.js web UI, and Apple Silicon Metal GPU acceleration — all running locally with zero cloud dependencies.

## Blog

- [Fine-Tuning a Tool-Calling Agent: SFT + QLoRA on Gemma 3 4B](https://aiwithvd.com/blog/fine-tuning-tool-calling-agent-sft-qlora-gemma): How I turned a prompt-driven ReAct agent over a 40+ tool MCP backend into a fine-tuned one — collecting multi-turn tool trajectories, building the SFT dataset, training Gemma 3 4B with QLoRA, and serving it back behind the agent loop. The case study that ties the whole fine-tuning series together.
- [LoRA & QLoRA: Fine-Tuning a Model That Doesn't Fit on Your GPU](https://aiwithvd.com/blog/lora-qlora-fine-tuning-one-gpu): Full fine-tuning a 7B model needs ~112GB before you load a batch. LoRA trains ~1% of the weights; QLoRA squeezes the frozen base into 4 bits so a large model fits on a single card. Here's how both work — and the four pieces of QLoRA interviewers always probe.
- [SFT: What the Model Is Actually Predicting (and the Mask That Decides If It Works)](https://aiwithvd.com/blog/sft-what-the-model-predicts): Supervised fine-tuning looks like 'teach the model the answer.' Mathematically that's not what happens. It's the same next-token prediction as pretraining — and two quiet details, response masking and the chat template, decide whether your fine-tune works or silently rots.
- [Inside a Transformer Block: Why Where Knowledge Lives Decides Where You Fine-Tune](https://aiwithvd.com/blog/inside-a-transformer-block): Most people learn q_proj, k_proj, v_proj, o_proj and think that's the whole transformer. It isn't. A block has two machines — attention and the MLP — and knowing which does what is the difference between a LoRA config that learns your domain and one that doesn't.
- [The Two Axes of Fine-Tuning: A Mental Model That Stops the Confusion](https://aiwithvd.com/blog/two-axes-of-fine-tuning): LoRA, SFT, QLoRA, DPO, PPO, GRPO — they all blur together until you see they live on two independent axes. One decides how you touch the weights, the other decides what signal you train on. The map I use before every fine-tuning project.
- [Using Mem0 for User Preference Memory and Context Switching in AI Agents](https://aiwithvd.com/blog/mem0-user-preference-memory-context-switching): How to store user preferences in Mem0, retrieve them by context tag before each response, and handle context switching when users shift between different tasks. Practical patterns with LangGraph integration.
- [Building a 4-Tier Verification Layer for LLM Outputs in Regulated Domains](https://aiwithvd.com/blog/building-verification-layer-llm-outputs): How I built a verification pipeline that catches hallucinated citations before they reach production. NLI cross-encoder, Claude judge, hard-fail gates, and the economics of getting it right at $0.05 per query.
- [The Rise of the Autonomous Company: Hermes, OpenClaw, and the Paperclip Revolution](https://aiwithvd.com/blog/the-rise-of-the-autonomous-company-hermes-openclaw-and-the-paperclip-revolution): A comparative analysis of the leading AI agent frameworks in May 2026: Hermes' self-improvement, OpenClaw's massive ecosystem, and Paperclip's orchestration.
- [Building a Self-Hosted Voice AI Assistant with LiveKit Agents](https://aiwithvd.com/blog/building-voice-ai-assistant-livekit): How I built a real-time conversational voice AI using LiveKit Agents, FastAPI, Whisper, Ollama, and Edge-TTS — fully self-hosted with no cloud dependencies, zero API costs, and sub-2s response latency.
- [Prompt Engineering vs RAG vs Fine-Tuning: The Decision Framework I Actually Use](https://aiwithvd.com/blog/prompt-rag-finetune-decision-framework): Stop guessing which LLM technique to use. Data-driven decision framework with ROI analysis, cost comparisons, and real production case studies from Document Extraction Pipeline and DeepAgent.
- [Evaluating Generative AI in Production: Metrics Beyond 'Correct' and 'Incorrect'](https://aiwithvd.com/blog/evaluating-generative-ai-production): Stop eyeballing LLM outputs. Learn production-grade evaluation: LLM-as-judge, deterministic checks, RAG faithfulness, and A/B testing frameworks that actually work.
- [Memory Management for AI Agents: Context Window Optimization Without Token Bankruptcy](https://aiwithvd.com/blog/ai-agent-memory-management): Stop letting context windows bankrupt your AI budget. Learn sliding window, summarization, and vectorized memory strategies with real cost savings from production deployments.
- [From Document Processing to LLM Resilience: Patterns That Scale](https://aiwithvd.com/blog/resilient-llm-api-patterns): Building on Document Extraction Pipeline's Celery/Redis foundation, learn to extend async patterns to LLM-specific resilience: circuit breakers, multi-provider fallbacks, and token bucket rate limiting.
- [Production-Grade LLM System Architecture: From Notebook to 10k RPM](https://aiwithvd.com/blog/production-llm-system-architecture): Learn how to design decoupled LLM systems that handle 10,000 requests per minute. Covers async queues, caching strategies, RAG integration, and the three biggest bottlenecks you'll face.
- [Building a Production-Ready LangChain DeepAgent with SSE Streaming](https://aiwithvd.com/blog/building-langchain-deepagent-sse-streaming): How I built a production AI agent API using LangGraph, Ollama, and FastAPI with real-time SSE streaming, Redis rate limiting, and multi-turn session memory.
- [OpenClaw: A Self-Hosted AI Assistant with Ollama, Telegram & Discord](https://aiwithvd.com/blog/openclaw-self-hosted-ai-assistant-guide): Install OpenClaw, wire Anthropic/OpenAI/Google or a local Ollama model, control it from Telegram and Discord, extend it with skills.sh, and turn it into a business gateway for lead qualification, customer support, and agent-ecosystem management.
- [Building a Production-Ready Document Extraction Pipeline with FastAPI and MinerU](https://aiwithvd.com/blog/building-document-extraction-pipeline-fastapi-ollama): How I built a high-performance document extraction system using FastAPI, Celery, MinerU, and LLMs. Learn the architecture, challenges, and lessons from deploying AI-powered document processing at scale.
- [Getting Started with LLM Agents in Production](https://aiwithvd.com/blog/getting-started-with-llm-agents): A practical guide to building and deploying LLM-powered agents using LangChain and FastAPI, with lessons from real-world implementations.

## Other

- [Blog index](https://aiwithvd.com/blog)
- [RSS feed](https://aiwithvd.com/rss.xml)
- [Sitemap](https://aiwithvd.com/sitemap.xml)