--- title: "Om Jaiswal - Software Engineer in Noida | Tal" description: "Meet Om Jaiswal, Full Stack & AI/ML Engineer | National Finalist- KAVACH Cyber Security Hackathon 2023| GenAI & Agentic AI | LLMs, RAG & LangGraph | MCP | Python | FastAPI | Multi-Agent | Node.js | Prompt Engineering | Docker & AWS based in Noida. AI stack includes antigravity, claude code, and cla..." image: "https://tal.club/meet/om-jaiswal/share-image" --- # Om Jaiswal on Tal ## Headline Full Stack & AI/ML Engineer \| National Finalist\- KAVACH Cyber Security Hackathon 2023\| GenAI & Agentic AI \| LLMs, RAG & LangGraph \| MCP \| Python \| FastAPI \| Multi\-Agent \| Node\.js \| Prompt Engineering \| Docker & AWS ## About π„π§π π’π§πžπžπ«π’π§π  𝐒𝐧𝐭𝐞π₯π₯𝐒𝐠𝐞𝐧𝐜𝐞, 𝐞𝐟𝐟𝐒𝐜𝐒𝐞𝐧𝐜𝐲, 𝐚𝐧𝐝 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐒𝐬𝐞 𝐒𝐦𝐩𝐚𝐜𝐭 \- π­π‘πšπ­β€™π¬ 𝐰𝐑𝐚𝐭 𝐝𝐫𝐒𝐯𝐞𝐬 𝐦𝐞 With 2\+ years of experience across AI/ML and full\-stack engineering, I build intelligent systems that simplify complexity, automate manual work, and speed up decisions\. My work spans agentic AI, retrieval\-augmented generation, and production backends deployed on AWS\. At Car Wizard \(VahanHelp\), I owned the AI layer of a live internal CRM used by operations and support teams to process 10,000\+ vehicle\-transfer cases for 8,000\+ customers and dealers\. I built semantic case search on pgvector that cut lookups from 5–10 minutes to under a second, shipped a RAG policy assistant served through ReAct agents, LangGraph state machines and MCP tools, and lifted end\-to\-end RAGAS scores from 0\.60 to 0\.93 with better chunking and cross\-encoder reranking\. Earlier, at DigiLocker, I built a facial anti\-spoofing system that reached 96\.4% classification accuracy and an EER under 2\.5%\. I pair that AI work with full\-stack ownership: async FastAPI services, React/TypeScript frontends, and a live React Native app\. Everything ships behind eval harnesses and shadow\-mode gates, so the systems are measurable, auditable, and safe to trust\. 𝐌𝐲 𝐬𝐭𝐫𝐞𝐧𝐠𝐭𝐑𝐬: \- Agentic AI: LangGraph, LangChain, ReAct loops, MCP, tool calling, structured outputs \- RAG & Retrieval: hybrid search \(dense \+ BM25\), chunking, cross\-encoder reranking, pgvector \- LLM Integration: Claude, OpenAI, prompt engineering, guardrails, human\-in\-the\-loop \- Evals & Observability: RAGAS, DeepEval, LangSmith, gold datasets \- ML & Computer Vision: PyTorch, Hugging Face, fine\-tuning \- Backend & Full Stack: Python, FastAPI, Node\.js, TypeScript, React, React Native, PostgreSQL, MongoDB \- Cloud & Deployment: AWS \(EC2, S3, Lambda, RDS, SageMaker\), GCP, Docker, Kubernetes π‡πžπ«πžβ€™π¬ 𝐰𝐑𝐚𝐭 𝐈 𝐛𝐫𝐒𝐧𝐠 𝐭𝐨 𝐭𝐑𝐞 π­πšπ›π₯𝐞: \- System Thinking: Architectures that connect data, retrieval, agents, and business workflows \- End\-to\-End Ownership: From embeddings and evals to APIs, apps, and deployment \- Execution Rigor: Prototype to production, measured rather than guessed Side projects: an email and calendar agent with prompt\-injection guardrails and approval gates, and QueryPilot, a text\-to\-SQL copilot that narrows a 500\-table schema with RAG\. National Finalist, KAVACH Cybersecurity Hackathon 2023\. CodeChef 1900\+\. Let’s connect if you’re building with LLMs, RAG, or AI agents\. I’m open to AI/ML Engineer, LLM/Agentic AI, and AI Backend roles\. ## Location Noida ## AI stack antigravity claude code claude codex cursor geminicli ## Work experience Software Engineer at VAHANHELP, May 2024 \- Present Β· 2 yrs 5 mos, Noida, \- Built the full admin/agent dashboard for a multi\-department CRM using Angular 17\+ \(standalone components\) on the frontend and Node\.js/Express, MongoDB on the backend, with RBAC and aggregation\-pipeline reporting\. Implemented real\-time features via WebSockets β€” live case/lead status updates and agent\-to\-agent chat β€” so teams could coordinate without manual refreshes or status calls\. Owned end\-to\-end architecture and AWS deployment, including a companion React Native app \(Play Store\) with OTP auth and Razorpay integration\. \- Built a semantic case\-similarity search feature for a production RC\-transfer case database: embedded case notes with OpenAI's text\-embedding\-3\-small into PostgreSQL/pgvector, exposed via FastAPI so agents retrieve similar past cases instantly instead of manual search \- Designed a RAG\-based policy assistant giving support staff conversational access to RTO transfer rules and SOPs, served through an MCP Python server exposing case\-search and policy\-lookup as callable tools β€” deployed on AWS EC2/Docker \- Re\-architected a bulk vehicle\-lookup workflow from 200 sequential API calls into 20 concurrency\-controlled batch calls with partial\-failure handling and exponential backoff \-cut processing time from 4 minutes to under 25 seconds \- Designed backend services and schema for a multi\-department CRM \(Node\.js/Express, MongoDB\) with RBAC and aggregation\-pipeline reporting; owned end\-to\-end architecture and AWS deployment of a live Play Store app \(React Native\) with OTP auth and Razorpay integration AI/ML Intern at DigiLocker, Mar 2024 \- May 2024 Β· 3 mos, Remote, During my internship at DigiLocker, I specialized in AI and ML, focusing on enhancing digital document security\. I developed a classifier using transfer learning to distinguish real from spoofed facial images with high accuracy\. Additionally, I engineered a facial antispoofing system from scratch, significantly improving detection accuracy\. Implementing dual\-technique approaches based on research papers, I contributed to creating a robust facial antispoofing mechanism, refining my skills in AI, ML, and cybersecurity\. ## Education Bachelor of Technology \- BTech, Dr\. A\.P\.J\. 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