Engineering practical AI systems, agentic workflows and enterprise solutions.
Chennai, India7+ years in enterprise technology
ENGINEER · BUILD · DELIVER
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FEATURED WORK
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Selected work
Projects & case studies
Selected engineering work across enterprise AI delivery, workflow automation and applied AI solution development.
Independent project · In progressEnterprise AI adoptionAI engineering operationsCustomer-specific delivery
AegisMike – AI Engineering Platform Project
AegisMike is an independent technical project exploring a modular environment for designing, validating and delivering AI solutions. The concept brings together solution discovery, project workspaces, data sources, controlled simulations, model and provider management, agent orchestration, analytics and governance. The focus is on engineering reusable workflows that can be adapted to different use cases and deployment requirements.
Deployment flexibility
Three deployment models
Choose the deployment approach that fits the customer's infrastructure and operating requirements.
Active subproject within AegisMike. A built engineering project for IT incident investigation, combining RAG, structured tool calling and ML-based escalation-risk prediction. Includes customer-profile, open-ticket, escalation-risk, vulnerability and knowledge-base tools; Sentence Transformers with Qdrant; and a Scikit-learn logistic-regression risk model.
Planned subprojectAegisFlight · Aviation Data Intelligence
Planned subproject within AegisMike. An aviation-focused data intelligence initiative centred on flight-recorder-style time series, flight phases, sensor behaviour and labelled anomaly scenarios.
Generate reproducible, phase-based flight data at 1 Hz.
Support large synthetic flight datasets with Parquet and CSV exports.
Inject labelled sensor anomalies and data-quality scenarios for analytics and AI evaluation.
Architecture direction: The internal AegisMike engineering environment is planned for GCP, including a simulation data library. Customer solutions are intended to be deployed independently in customer-managed environments. These capabilities describe the product vision and roadmap; they are not represented as completed or generally available functionality.
Designed and implemented within my ManageEngine Analytics Plus role to bring product knowledge, senior-technician troubleshooting practices and AI-assisted communication directly into customer and technician support workflows. The suite contains two distinct solutions: a customer-facing product-support chatbot for Analytics Plus, and a technician-facing AI copilot embedded in Zoho Desk.
Customer-facing chatbotWebsite and in-product support · approximately 80% chat coverage · approximately 90% success rate
Unified Solution & Email AssistantChatGPT-like Zoho Desk interface for product questions, solution discovery and customer-facing technical email drafting
1. Customer-facing Analytics Plus chatbot
Reported impact: Handles approximately 80% of incoming support chats, with an approximately 90% success rate.
Available where customers need it: Accessible from the public ManageEngine Analytics Plus website and embedded within the Analytics Plus application.
Knowledge-grounded product assistance: Answers Analytics Plus product questions using product documentation, knowledge-base articles, release notes and troubleshooting guides.
Self-service support: Helps customers resolve common product questions through conversational assistance, reducing the need to wait for a support technician.
2. Technician-facing AI copilot — inside Zoho Desk
Reported impact: Approximately 50% reduction in technician workload by reducing repeated manual research, accelerating response preparation and making experienced troubleshooting knowledge easier to access.
AI First Response: Drafts an initial customer response based on the ticket context and relevant product knowledge.
Senior-technician-style investigation guidance: Generates separate internal notes recommending what to check next, likely investigation paths and practical troubleshooting steps. This helps newer technicians follow structured investigation approaches and learn from established support practices.
Historical Ticket Retrieval: Uses a dedicated retrieval capability to find similar past tickets and surface their investigation steps and resolution context, helping technicians reuse relevant case knowledge instead of starting from scratch.
AI Thread Replier: Drafts contextual follow-up replies for subsequent messages in an existing ticket conversation, keeping the response aligned with the ticket history.
Unified Solution & Email Assistant: Provides a single ChatGPT-like interface inside Zoho Desk where technicians can ask product and troubleshooting questions, discover relevant solutions and draft customer-facing technical emails.
Knowledge and retrieval foundation: Product documentation, knowledge-base articles, release notes and troubleshooting guides ground product-support responses. Historical-ticket retrieval separately brings similar past cases into the technician First Response workflow.
Implementation: Analytics Plus website and in-application chatbot experiences, Zoho Desk integration, Deluge automation, dedicated technician workflows, retrieval tools and LLM-powered assistance.
What I work with
Engineering capabilities
Engineering focus: designing, building, integrating and evaluating AI systems that solve practical problems and lower the barriers to enterprise AI adoption.
Business use-case discovery, enterprise account ownership, analytics delivery, AI engineering, automation and stakeholder management.
Aug 2022 – Present · Zoho Corporation · Chennai
Senior Technical Consultant (ETAM) | ManageEngine Analytics Plus
While my role spans enterprise technical consulting and analytics, my primary focus over the past three years has been designing and delivering workflow automations and deploying AI-powered solutions for real business and support use cases.
Automation & AI solution deployment: Lead the design and implementation of workflow automations, AI assistants and agentic solutions, taking use cases from discovery through integration, deployment and adoption.
Business use-case discovery: Work with enterprise customers and internal stakeholders to understand operational challenges, map business processes and translate requirements into practical automation and AI solutions.
Enterprise account & engagement management: Own technical engagements, manage key customer relationships, coordinate escalations and align solution outcomes with customer expectations.
Analytics & dashboard engineering: Delivered 500+ interactive dashboards, defined business and ITSM KPIs, and developed SQL-based reporting for operational visibility and decision-making.
AI engineering: Build RAG-powered assistants, AI copilots and agentic workflows that connect enterprise knowledge, retrieval capabilities and business tools to support processes.
Integration & stakeholder delivery: Integrate systems using APIs, Zoho Deluge, MCP and related technologies; lead technical discussions, coordinate cross-functional teams and drive delivery through to resolution.
Apr 2021 – Jul 2022 · Tech Mahindra · Chennai
Associate Technical Support
Provided Level 2 support for application, infrastructure and integration incidents within SLA commitments. Used SQL analysis and structured troubleshooting to diagnose incidents and coordinate escalations.
Jan 2018 – Mar 2019 · Cyient Limited · Bengaluru
Assistant Design Engineer
Developed Aircraft Maintenance Manuals (AMM) and Illustrated Parts Catalogues (IPC), with focus on technical accuracy and regulatory compliance.
Mar 2017 – Jan 2018 · Hindustan Aeronautics Limited (HAL) · Bengaluru
Graduate Apprenticeship Trainee
Supported maintenance and overhaul of aircraft gas turbine engines, applying structured troubleshooting, root-cause analysis and engineering practices.
The bigger picture
Making enterprise AI practical
The focus is not AI for its own sake. It is helping teams adopt AI responsibly, connect it to their data and turn it into useful business workflows.
Approach
Build around the business problem
Start with the workflow, data, user and measurable outcome. Choose models and architecture to serve that need.
Principle
Keep organizations in control
Design for customer-owned environments, configurable providers, human approval and clear operational visibility.
Education & credentials
Engineering foundation
An aeronautical engineering background combined with continuous learning in software, data and AI technologies.