Prajwal S.

I build production-grade SaaS platforms and agentic AI systems that connect LLMs to real business data, documents, APIs, tools, and approval workflows. With 10+ years in software engineering and technical leadership, I bring full-stack development, AI/ML, system architecture, and DevOps experience to the same project. I can define the architecture, build the product, integrate it with existing systems, and deploy it as a secure and maintainable production service. My experience includes multi-tenant SaaS, insurance and telecom platforms, payment and billing integrations, AI agents, RAG systems, data pipelines, Kubernetes infrastructure, and production troubleshooting. Agentic AI and LLM systems: I build AI applications that can retrieve information, reason across documents, call tools, execute multi-step workflows, and involve humans when decisions require review. - Agent architecture and orchestration using LangGraph, Deep Agents, LangChain, LlamaIndex, and CrewAI - Tool calling across APIs, databases, internal services, and business applications - MCP servers and integrations for connecting agents with external capabilities - RAG pipelines for documents, knowledge bases, and structured business data - Multi-document projects with retrieval, context management, and source attribution - Human-in-the-loop approvals for sensitive or consequential operations - AI workflow automation using n8n and custom services - Evaluation harnesses for answer quality, task completion, regressions, and edge cases - Agent tracing, observability, retries, permission controls, and production safeguards - AI copilots, voice-enabled workflows, and domain-specific assistants - Streaming AI interfaces using Next.js and the Vercel AI SDK I focus on the complete agent harness, not just the prompt - including tools, memory, context, permissions, execution, evaluation, and monitoring. SaaS and product engineering: - Full-stack products from initial architecture through production - Multi-tenant SaaS platforms and configurable business workflows - Subscription billing, invoicing, credits, and payment gateway integrations - Complex APIs, webhooks, background jobs, and data synchronization - Dashboards, analytics, reporting, and data pipelines - Performance, security, scalability, and reliability improvements - Modernization of existing platforms without unnecessary rewrites Architecture, cloud, and DevOps: - Backend architecture, distributed systems, and microservices - Docker, Kubernetes, Helm, NGINX, and CI/CD pipelines - AWS, DigitalOcean, GCP, and Vercel deployments - ELK/EFK, Prometheus, Grafana, tracing, and operational monitoring - Production incident investigation, scaling, and cloud-cost optimization Core technologies: Languages: Python, PHP, JavaScript/TypeScript, SQL Frameworks: FastAPI, Laravel, Node.js, React, Next.js AI: Frontier models and tools, LangChain, LangGraph, Deep Agents, LlamaIndex, CrewAI, RAG, MCP, Vercel AI SDK Data and automation: PostgreSQL, MySQL, Redis, Pinecone, Chroma, n8n Infrastructure: AWS, DigitalOcean, GCP, Vercel, Docker, Kubernetes, Helm, NGINX, GitHub Actions How I work: I begin with the business workflow, risk, data, and operational constraints. I then choose the simplest architecture that can be tested, maintained, and scaled. I do not force an AI solution where deterministic software would be safer or more effective. I communicate clearly, provide working demonstrations, document important decisions, and deliver in measurable stages so progress remains visible. I work particularly well with: - Founders building or scaling SaaS products - Teams adding AI capabilities to existing platforms - Companies automating complex document or operational workflows - Projects requiring both hands-on implementation and technical leadership If you are building a SaaS platform, production AI agent, RAG system, or complex business automation, tell me what the system must accomplish and which tools and data it needs to work with.