I architect autonomous AI systems that ship without human intervention
20+ years driving digital transformation across healthcare, SaaS, and AI. Co-founder of B24 e Solutions, ex-CTO at Spericorn, and now building autonomous multi-agent platforms with LangGraph, CrewAI, and MCP.
Trusted by teams at
Across banking, healthcare, manufacturing, retail, and AI sectors since 2004
Consistently 5-star ratings across 50+ projects with global clients
Led engineering teams of up to 35 as CTO at Spericorn Technology
From co-founding B24 e Solutions in 2004 to delivering AI products globally in 2026
Fidelity, CBRE, PNB MetLife, Godrej, Meesho, SkinIQ, and more
Remote delivery across India, United States, United Kingdom, and Australia
Virendra Vaishnav is an Agile Technical Delivery Manager and Certified ScrumMaster based in Jaipur, India, with over 20 years of experience driving digital transformation across healthcare, SaaS, AI, and enterprise platforms. He leads cross-functional distributed teams using Scrum, Kanban, and SAFe to deliver high-value products on time and within budget.
My journey started as Co-Founder & Technical Lead of B24 e Solutions in 2004, where I delivered 150+ projects across banking, healthcare, manufacturing, and retail over 11 years. I went on to manage agile delivery at Techjini for clients like Fidelity, CBRE, and PNB MetLife, served as CTO at Spericorn Technology leading 35+ engineers, and now work as a Senior Project Manager on Upwork with a 95% success rate across 50+ projects.
On the side, I build AI-powered products with Agentic AI, LangGraph, CrewAI, RAG, and MCP. I believe AI should solve real business problems, not create complexity. When I'm not shipping products, you'll find me mentoring young developers or exploring the latest in generative AI.
I don't just manage projects — I design the systems that make autonomous delivery possible. Here's how I think about building AI-native platforms.
Every complex system is decomposed into specialized agents with clear responsibilities. A planner agent, a builder agent, a reviewer agent, a deployer agent. They communicate through structured protocols, not chat. Each agent has a confidence threshold — if it's not sure, it escalates instead of guessing.
Before any agent acts, it evaluates two things: how confident is it in the action, and what's the blast radius if it's wrong? Low-risk actions execute autonomously. High-risk actions require explicit approval or a second-agent review. This is how you get AI that's bold on small changes and cautious on big ones.
Not every task needs GPT-4. I route tasks to the right model: fast/cheap models for classification, mid-tier for drafting, top-tier for complex reasoning. Token budgets are set per agent. The system optimizes for output quality per dollar, not raw capability.
My systems don't just execute — they learn. Every success and failure is logged in a structured memory. The next time a similar task appears, the system already knows what worked, what didn't, and what to try differently. Not fine-tuning — runtime learning through RAG-backed experience retrieval.
I design systems where specialized AI agents collaborate autonomously — planning, executing, and self-correcting without human-in-the-loop. Each agent owns a domain: code generation, testing, deployment, compliance. They negotiate, delegate, and resolve conflicts through structured protocols.
Full software lifecycle automation: AI agents that plan features, write code, run tests, handle deployments, and manage rollbacks. Not CI/CD tooling — actual autonomous systems that understand intent, manage risk, and ship production-grade software across regulated environments.
AI systems that treat regulatory compliance as a first-class constraint, not an afterthought. Built-in guardrails for healthcare (HIPAA), finance, and multi-jurisdictional requirements. The system knows what it can and can't do before it acts.
Every AI call has a cost. I architect systems with token budgets, model routing (use GPT-4 when accuracy matters, Haiku when speed does), and intelligent caching. The goal: enterprise-grade AI output at startup-grade costs.
Conceptual representation of autonomous platform architecture
Designed and built a multi-agent platform with 40+ modules handling the full software lifecycle: planning, building, testing, shipping, and regulatory compliance across 18+ countries. Proof that one architect with modern AI tools can build what traditionally requires a team of 20.
Enterprise multi-agent workflows with CrewAI — automated HR, finance, support, and sales processes using autonomous AI agents that collaborate to complete complex business tasks.
Multi-agent orchestration pipeline using LangGraph for complex reasoning tasks. Agents collaborate through a graph-based workflow with state management and conditional routing.
Model Context Protocol (MCP) server collection — weather, search, code execution, browser automation, and database tools for AI agents to interact with external services.
AI-powered CRM automation using n8n workflows. Automates lead scoring, follow-ups, customer segmentation, and sales pipeline management with intelligent AI agents.
Production-ready AI SaaS starter with Next.js 15, Vercel AI SDK, Supabase for auth & database, and Stripe for payments. Everything needed to launch an AI-powered SaaS product.
Virendra made my offshore experience run smoothly, adding to his very good technical and business knowledge, he is a great communicator, reliable and trustworthy.
VIRENDRA IS SMART. I CANNOT OVERSTATE THAT. THIS IS MY SECOND TIME WORKING WITH HIM. PLAN TO RETAIN FOR ONGOING WORK.
Virendra has provided valuable and reliable technical services to my company for multiple projects. He is always accessible and responsive with the solutions needed, both in the moment and for the long term. I highly recommend him.
Virendra did a wonderful job for us. He always solved problems with a clear mind and quick. He never had an issue working longer if needed.
End-to-end project delivery using Scrum, Kanban, or SAFe. Sprint planning, backlog grooming, retrospectives, and stakeholder management for teams of 5-35.
Building AI-powered applications using LangGraph, CrewAI, RAG, and MCP. From prototype to production-ready AI agents for enterprise automation.
Fractional CTO services including engineering team setup, architecture reviews, technology strategy, and technical due diligence for startups and scale-ups.
Legacy system modernization, cloud migration, DevOps implementation, and process optimization for organizations embracing modern technology.
After managing 50+ remote projects, I've found that async standups, overlapping work windows, and documented sprint goals matter more than synchronous ceremonies. The key is building team rituals that respect time zones while maintaining accountability.
Most enterprises should start with RAG, not fine-tuning. RAG gives you control over data freshness, costs less to iterate, and avoids the risks of model drift. Fine-tuning makes sense only when you need consistent behavioral patterns, not just knowledge retrieval.
Healthcare software fails when teams build for compliance first and users second. I've delivered platforms like TrackMx and Swift-S by flipping that order: build for the clinician, then layer in compliance. Agile's iterative approach catches regulatory gaps early without sacrificing UX.
I write about the intersection of AI engineering, agile delivery, and what I learn from building autonomous systems. Published on Dev.to, Hashnode, and LinkedIn.
The architecture decisions, agent coordination patterns, and hard-won lessons from building a multi-agent system that handles the full software lifecycle.
Read on Dev.to →Why blast radius analysis and confidence thresholds are the missing piece in most AI agent architectures — and how to implement them.
Read on Hashnode →After building both, here's the real-world decision matrix for choosing between RAG and fine-tuning — including the cases where the answer is neither.
Read on LinkedIn →Virendra Vaishnav is an Agile Technical Delivery Manager and Certified ScrumMaster (CSM) with over 20 years of experience driving digital transformation across healthcare, SaaS, AI, and enterprise platforms. He co-founded B24 e Solutions in 2004 where he delivered 150+ projects, served as CTO at Spericorn Technology, and currently works as a Senior Project Manager on Upwork with a 95% success rate across 50+ projects.
Virendra specializes in Agile project and program management using Scrum, Kanban, and SAFe. He has deep expertise in stakeholder management, risk mitigation, cross-functional team leadership, and delivering products across healthcare, SaaS, and AI domains. He also builds AI-powered tools using LangGraph, CrewAI, RAG, and MCP.
Yes. Virendra has extensive experience managing remote distributed teams across the US, UK, and India. On Upwork alone, he has completed 50+ projects with a 95% success rate, leading cross-functional global teams of up to 15 members using Scrum, Kanban, and SAFe methodologies with tools like Jira, Confluence, Trello, and Monday.com.
Virendra has delivered multiple healthcare projects including TrackMx, an AI-powered Healthcare SaaS platform where he led agile delivery managing a team of 9, and Swift-S, a Healthcare Platform where he oversaw delivery with a team of 12. He also managed delivery of an AI-powered skincare diagnostics platform at SkinIQ Inc.
Yes. Virendra actively builds AI agent systems using CrewAI for multi-agent enterprise workflows and LangGraph for complex reasoning pipelines. His open-source projects include CrewAI Enterprise Agents for automated HR, finance, and sales processes, and a LangGraph Multi-Agent Pipeline for graph-based workflow orchestration with conditional routing.
Virendra builds AI products using LangChain, LangGraph, CrewAI, RAG, MCP, OpenAI, Anthropic Claude, Google Gemini, LLaMA, Mistral, Vercel AI SDK, TensorFlow, PyTorch, n8n, Vector Databases, and Embeddings. For full-stack development, he uses Next.js, React, Python, FastAPI, Node.js, and TypeScript.
Virendra Vaishnav maintains a 95% Job Success Score on Upwork with over 50 completed projects since February 2020. He leads cross-functional global teams and has earned consistent 5-star reviews for delivering SaaS, healthcare, and AI projects on time and within budget.
Virendra offers four core services: (1) Agile Project Management using Scrum, Kanban, and SAFe for teams of 5-35; (2) AI Product Development building applications with LangGraph, CrewAI, RAG, and MCP; (3) CTO-as-a-Service for engineering team setup, architecture reviews, and technology strategy; and (4) Digital Transformation including cloud migration, DevOps, and microservices architecture.
Virendra follows a proven 5-step delivery methodology: (1) Discovery & Requirements with stakeholder interviews and roadmap creation; (2) Architecture & Sprint Planning with technology selection and backlog creation; (3) Agile Execution using 2-week sprints with daily standups and CI/CD; (4) Quality & Risk Management with automated testing and security audits; (5) Delivery & Iteration with staged rollouts and retrospectives.
Virendra Vaishnav is based in Jaipur, Rajasthan, India. He works remotely with clients across the United States, United Kingdom, Australia, and India. His experience managing distributed teams across multiple time zones makes him effective for global remote engagements.
You can hire Virendra through Upwork (where he maintains a 95% Job Success Score), by emailing virendra@aixpertz.ai, or by connecting on LinkedIn at linkedin.com/in/virendrakvaishnav. He is available for both project-based engagements and ongoing fractional PM/CTO roles. He typically responds within 24 hours.
Whether you need an agile delivery leader, an AI product builder, or a fractional CTO, I'm here to help turn your vision into reality. Available for remote engagements worldwide.