TANISH KHANDELWAL

Software Engineer · Client Delivery & Applied AI

Pune, India | +91-7378427998 | tanishkhandelwaltk012@gmail.com | linkedin/tknishh | github/tknishh | ktanish.in

Professional Summary

Software Engineer with 2+ years of experience building reliable, scalable APIs and full-stack products in Python and Node.js/TypeScript — specializing in client-facing delivery and applied AI. Experience with event-driven microservices (AWS Lambda, SQS, DynamoDB Streams) and applied AI systems — agent orchestration (CrewAI, LangChain, LangGraph), RAG pipelines, vector search, MCP-based tool integration, and workflow automation (n8n) for CRM integrations. Works directly with stakeholders and end users to turn requirements into shipped features, and treats code review as a core part of the process, not an afterthought. Uses AI-assisted development tools like Cursor and Claude Code to ship reliable software faster. Comfortable operating independently in a remote-first environment and picking up new languages, frameworks, and tools quickly.

Experience

Applied AI Engineer · The Cloud Intelligence Inc.

Applied AI Engineer · The Cloud Intelligence Inc.

  • Led technical discovery and solution design directly with the customer at SMSA Express — Saudi Arabia's leading courier and logistics conglomerate (230+ countries, 3,000+ couriers) — translating stakeholder requirements into a conversational Shopping AI Assistant integrated with SMSA's existing production APIs.
  • Contributed to AI feature development for miDoc, TCI's flagship AI-powered healthcare platform, engineering miDoc Cortex — a multi-turn conversational agent that allows patients and doctors to perform any action available in the miDoc UI (updating doctor notes, logging patient profiles, surfacing health trends) entirely through natural language conversation.
  • Built miDoc Cortex as a full agentic system with MCP (Model Context Protocol) server integration, exposing all miDoc platform APIs as structured tools; the agent resolves user intents, selects the appropriate tool, and confirms actions before execution — enabling safe, human-in-the-loop automation of healthcare workflows.
  • Implemented agent orchestration layer coordinating multi-step reasoning, intent disambiguation, and tool chaining across miDoc's full API surface — covering patient profile management, consultation note updates, appointment context enrichment, and medication retrieval through GenAI-backed knowledge bases.
  • Contribute as instructor and mentor for TCI's AI Practitioner Programme — an 8-week, developer-focused programme that takes engineers from concept to a deployable production-grade AI agent — running hands-on sessions, pairing with trainees on real production codebases, and reviewing their code through daily follow-ups.

Tech Stack: Python, FastAPI, Node.js, TypeScript, LangChain, LangGraph, MCP, CrewAI, RAG Pipelines, Vector Search, OpenAI / Anthropic APIs, PostgreSQL, REST API Design

Full Stack Developer · Finanshels

Full Stack Developer · Finanshels

  • Worked directly with end users across sales, delivery, finance, and accounting to run requirement-gathering and discovery sessions, map existing workflows and constraints, and translate them into system design for the Practice Management platform — owning solutions from discovery through production rollout, user training, and iteration.
  • Built core modules of a comprehensive Practice Management platform (Next.js + NestJS) automating full-cycle organisational workflows — bridging sales pipelines, project management, and accounting systems — with PostgreSQL schemas and DynamoDB Streams optimised for high-throughput billing workflows.
  • Built a production LLM-powered AI Bookkeeper using a multi-agent orchestrator (CrewAI + LangChain); designed LLM-ready APIs and RAG pipelines with vector retrieval, reducing bookkeeping TAT by over 90% (weeks to under one day).
  • Implemented end-to-end LLM evaluation pipelines tracking retrieval precision, tool-calling accuracy, and hallucination rates in live environments using Pytest — enabling continuous quality improvement on AI workflows.
  • Built and deployed a secure, centralised client portal used across account management, project delivery, and finance teams — integrating unified client profiles, timesheet tracking, task management, and automated report delivery.
  • Built third-party integrations with accounting platforms, CRMs (Zoho), and billing systems using workflow-automation tools (n8n) — designing normalised data pipelines and webhook-driven sync layers to keep interconnected systems in real-time consistency.
  • Built and maintained event-driven microservices on AWS (Lambda, DynamoDB Streams, SQS) — powering the AI Bookkeeper — ensuring fault tolerance, high availability, and low-latency state propagation across interconnected financial data pipelines.
  • Integrated MCP (Model Context Protocol) servers for secure, standardised tool access across agent workflows — enabling scalable inter-agent communication and external API orchestration.
  • Worked closely with engineering leadership on CI/CD deployment pipelines and sprint planning, stepping in to coordinate day-to-day engineering operations during a leadership transition.
  • Handled incident management, root cause analysis, and observability setup; participated in code reviews and design discussions to uphold API-first, LLM-ready design principles across the platform.
  • Authored and maintained comprehensive Swagger / OpenAPI documentation across all platform APIs; implemented OAuth2 / JWT authentication flows for secure client portal and third-party integration access.

Tech Stack: Python, Next.js, NestJS, TypeScript, React, Tailwind, PostgreSQL, DynamoDB, AWS (Lambda, SQS, S3), Azure DevOps, CrewAI, LangChain, LangGraph, Docker, Redis, pgvector, Pytest, Swagger/OpenAPI, OAuth2/JWT

Python Developer (Backend) · The Good Glamm Group

Python Developer (Backend) · The Good Glamm Group

  • Migrated legacy monoliths to event-driven microservices on Docker/AWS; designed async SQS/SNS pipelines for real-time inventory and order state propagation serving millions of concurrent users.
  • Built and optimised FastAPI / Fastify / Flask backend services using BFF (Backend for Frontend) pattern to decouple UI and backend logic; improved p99 latency by 35% on high-traffic read paths through targeted SQL and NoSQL query optimisation (PostgreSQL, MongoDB).
  • Integrated AI-driven semantic search using embeddings and vector retrieval to boost product discovery relevance; improved recommendation quality measurably via A/B testing.
  • Streamlined CI/CD pipelines (GitHub Actions) reducing release cycles by 40%, enabling safe, rapid feature rollouts with automated test coverage gates.

Tech Stack: Python, FastAPI, Flask, Node.js (Fastify), MongoDB, PostgreSQL, Redis, AWS (SQS/SNS, EC2), Azure DevOps, Docker, LLM APIs

Products

loopvoice.aiAI-Powered Voice Automation Platform for Shopify
Live Product

loopvoice.ai — AI-Powered Voice Automation Platform for Shopify | Live Product | Built in 1.5 months

  • Built and shipped a fully live AI voice automation platform for Shopify stores in 1.5 months using AI-assisted tooling (Cursor, Claude Code) — integrating voice AI APIs (STT/TTS) to automate abandoned cart recovery, order follow-ups, and customer outreach via automated voice calls, email, and SMS.
  • Designed a DAG-based visual flow builder — modelled flows as Directed Acyclic Graphs (DAGs) to enable merchants to create event-triggered automation (abandoned checkout, order placed, etc.) with configurable wait nodes and branching logic — integrated Redis-based job scheduling for reliable timed execution.
  • Built custom AI voice agents with RAG-powered knowledge bases (ingesting store websites and policies) to handle real customer queries — and a campaign feature for batch-calling segmented customer lists with configurable call frequency and rate controls.
  • Integrated Telnyx for in-platform phone number provisioning across global regions; full-stack built on Next.js, PostgreSQL, GCP, and Redis.

Next.js, PostgreSQL, GCP, Redis

bot9.aiEnterprise Customer Support Chatbot Platform
Used by RentoMojo PAN India

bot9.ai — Enterprise Customer Support Chatbot Platform | Used by RentoMojo PAN India

  • Single point of contact (POC) for RentoMojo's end-to-end PAN-India deployment — owned technical discovery, integration design against their production systems, configuration, production rollout, and post-deployment support with the customer's engineering and support teams.
  • Built a production-grade enterprise chatbot platform currently deployed for RentoMojo across India — supports configurable bot logic, team member management, and omnichannel integrations (Slack, Discord, Freshchat, WhatsApp).
  • Enabled direct API integration with enterprise production databases — allowing the bot to act on live data (orders, rentals, accounts) rather than static knowledge; powered by Typesense (vector search) for semantic retrieval and built on Node.js, React, and PostgreSQL.

Node.js, React, PostgreSQL

Projects

OpenAGIOpen-Source Autonomous Agent Framework

OpenAGIOpen-Source Autonomous Agent Framework | Feb 2024

  • Built core backend infrastructure for an open-source autonomous agent framework; implemented MCP servers enabling secure, scalable tool integration and inter-agent communication across distributed workflows.
  • Engineered custom planning algorithms, memory-augmented state machines, and self-correcting execution pipelines with fault recovery — applied Control Plane architecture to enable long-running agentic workflows.
LegalEaseCompliance & Document Automation Platform for MSMEs

LegalEaseCompliance & Document Automation Platform for MSMEs | Nov 2023

  • Designed a scalable FastAPI backend with automated OCR ingestion pipelines, RESTful microservices, and semantic document retrieval using vector embeddings and pgvector.
  • Implemented secure data storage and retrieval with AWS S3 and IAM role-based access controls, ensuring enterprise-grade data privacy and encryption compliance.

Skills

Languages: Python (Expert); TypeScript / JavaScript / Node.js (NestJS, Fastify); Java (working knowledge)

Backend & APIs: FastAPI, Django, NestJS, Flask — REST API design, async programming, API-first architecture, Swagger/OpenAPI, OAuth2/JWT, BFF pattern

Databases: PostgreSQL, MongoDB, DynamoDB, Redis, Neo4j (Certified Professional), pgvector / vector DBs, SQL — schema design, query optimisation

Cloud & Distributed Systems: Microservices, Event-Driven Architecture, AWS (Lambda, SQS/SNS, DynamoDB Streams, S3, EC2), Docker, Kubernetes, GCP, Vercel, Azure (App Services, DevOps, Blob Storage)

Applied AI / LLM: LLM Agent Orchestration (CrewAI, LangChain, LangGraph), RAG Pipelines, Vector Search, NLP (Conversational AI, Intent Recognition), Prompt Engineering, LLM Evaluation, MCP, Embedding APIs (OpenAI, Anthropic), Semantic Search

Engineering Practices: System Design, Workflow Automation (n8n), CI/CD (GitHub Actions, Azure DevOps), Observability & Alerting, Incident Management, TDD, Pytest, Code Reviews

Customer-Facing Delivery: Requirement Gathering, Technical Discovery, Solution Design, Stakeholder Communication, Deployment Ownership, User Onboarding, Technical Documentation

Education

B.Tech, Computer ScienceSpecialisation in AI/ML · CGPA: 8.5 / 10

Jaypee University of Engineering and Technology, Guna2020 – 2024

Certifications

  • Neo4j Certified ProfessionalNeo4j
  • TensorFlow for AI, ML & Deep LearningCoursera / deeplearning.ai