Manish Jaiswal

Developed AI-powered ServiceNow ticket automation system for enterprise IT operations. Utilized FastAPI, LangGraph, Azure OpenAI, and PostgreSQL in technology stack. Integrated REST API and Power Automate to enhance overall system functionality. Employed Python 3.12 to optimize backend processes and boost performance.

Engineered and deployed end-to-end AI-driven email-to-ServiceNow ticket automation platform that intelligently classifies incoming emails, extracts structured data utilizing LLM orchestration, validates business rules, conducts duplicate detection, and automatically generates INC/SR/CHG tickets in ServiceNow. Utilizes LangGraph multi-node workflow orchestration with Azure OpenAI (LLM + Vision) for asynchronous background processing and stringent guardrail validation to ensure enterprise-grade reliability and compliance. Minimized manual ticket triaging effort through automation of classification field extraction and ticket creation with intelligent duplicate prevention and contextual email notifications.

Roles & Responsibilities

• Designed end-to-end asynchronous microservice architecture using FastAPI and LangGraph.
• Architected a 4-node stateful AI workflow (Guardrails → Image Analysis → Field Extraction → Ticket Ingestion).
• Implemented enterprise-grade duplicate detection using conversation threading and job-state tracking.
• Designed PostgreSQL schema for job tracking, state transitions, audit history, and business metrics.

• HCP Precision: HIV Market Analytics:
• HCP persona/micro segmentation for US based company in HIV market based on personal and mass market promotion channel data, product sales, competitor sales and patient therapy switch data.
• The role revolves around data gathering, data pre-processing, feature engineering, classification model building and presenting results to marketing team.
• The predicted persona characteristics will help medical representatives to plan content for HCPs to uplift the bottom line.
• Data Genie (ETL Pipeline): Data Ingestion from Amazon S3 Cloud platform to Redshift table and transforming ingested data for further processing.

• Anticoagulant Opportunity Mapper:
• Facility Targeting for Anti-Coagulant Market Built a pipeline for identifying the High value Facility having Opportunities for Anti-Coagulation Drugs. Data analysis and ML classification model.
• Helped to increase in market share and overall engagement with High value Facility.

• Marketing Retention Campaign Model: Developed end-to-end Machine Learning Predictive model on ~3M Medicare population to predict probability of a member going to lapse health plans. Prediction on the basis of various member’s demographics and behavioral data. Model prediction are utilized by marketing team to run campaign on members. This model helped marketing team save cost in campaigning.
• Claims Fraud detection: Fraud Detection among the thousands of daily claims received, our role in this project is to build data science utility and predict the fraudulent claims. Gathered data from different data sources and load it to the central repository, data preprocessing and developed Machine Learning Models.
• Claims Highway: Claims highway is a flexible, re-usable, enterprise-wide claim system architecture simplifies system integrations and allows for common claims services to be shared by multiple systems, thereby increasing consistency, and reducing costs.
• Role and responsibility: Mainframe Developer
• Supporting and resolving/fixing batch abends within SLA.
• Solving the Incidents within SLA.
• Performing Unit testing and capturing the Unit test results.
• Client interaction and Requirement Gathering.