HYDERABAD, INDIA

ATHARVA
UDAVANT

I build production agents with LangGraph, LLMs, and Phoenix.

01 Timeline

Experience

  1. August 2025 - Present

    Software Engineer, AI/ML

    Align Technology · Hyderabad, India

    Agentic AILangGraphLLMPhoenix
    • Owned the architecture, development, integration, and production rollout of an AI Scrum Master agent using Claude, LangGraph, and MCP, translating stakeholder requirements into agentic workflows and tool calls across ServiceNow and Microsoft Graph APIs.
    • Led end-to-end delivery of the Bulk KB Article Creator, partnering with business users from requirements discovery and solution design through implementation, UAT, production deployment, and support. Translated high-volume needs into validation rules, workflow logic, and API-driven automation.
    • Built the ServiceNow Employee Self Service Agent in MS Teams, combining sub-second RAG over 2,000+ KB articles, in-chat catalog submissions, proactive ticket updates, and role-tailored views for ICs and Managers.
    • Built an AI employee referral workflow integrating Microsoft Forms, SharePoint, LLM resume parsing, skill classification, automated acknowledgements, and referral-status tracking.
  2. April 2024 - July 2025

    Software Engineer

    Locobuzz Solutions Pvt. Ltd. · Mumbai, India

    KafkaPythonAirflowDynamoDB
    • Designed and supported high-throughput Python microservices and data pipelines for operational, analytics, and AI workloads, handling production debugging, error resolution, and performance optimization.
    • Architected a highly available ETL pipeline using Apache Kafka, implementing scalable ingestion, transformation, and failure handling that reduced processing time by 80%.
    • Developed scheduled and on-demand Apache Airflow workflows with validation and error handling, reducing reporting latency by 80% and improving pipeline reliability.
    • Replaced inefficient Redis scans with a multi-level DynamoDB caching strategy, reducing CPU utilization and improving infrastructure efficiency.

02 Toolkit

Skills

Agentic AI & Systems

Architecting autonomous multi-agent loops and state machines with LangGraph and MCP, connecting LLMs to enterprise tools like ServiceNow and Microsoft Graph.

Core Technologies & Tools

LangGraphLangChainModel Context ProtocolTool CallingMulti-Agent RoutingState Persistence

Generative AI & LLMs

Deploying production LLM workflows using Claude, Azure OpenAI, and open models, specializing in structured schema outputs, prompt optimization, and task-specific fine-tuning.

Core Technologies & Tools

ClaudeAzure OpenAIOpenAI APIStructured OutputsPrompt EngineeringLLM Fine-Tuning

RAG & Knowledge Retrieval

Engineering low-latency retrieval systems with dense vector search, semantic embeddings, custom chunking algorithms, and CDC-based knowledge synchronization.

Core Technologies & Tools

EmbeddingsSemantic SearchVector SearchChromaDBAstraDBCDC Ingestion

AI Observability & Reliability

Instrumenting agentic and LLM applications with Arize Phoenix and OpenTelemetry for distributed trace inspection, latency monitoring, and production debugging.

Core Technologies & Tools

Arize PhoenixOpenTelemetryLatency ProfilingTrace InspectionProduction MonitoringError Resilience

Backend & Event Streaming

Building high-throughput asynchronous Python microservices with FastAPI, scalable Apache Kafka event streams, and Airflow orchestration for resilient data processing.

Core Technologies & Tools

FastAPIPython MicroservicesApache KafkaAirflowREST APIsBackground Jobs

Cloud & Production DevOps

Containerizing and deploying resilient cloud workloads across Azure and AWS with Docker, automated CI/CD pipelines, and serverless compute.

Core Technologies & Tools

AzureAWSDockerLinuxServerlessGit/GitHub Actions

Databases & Vector Stores

Designing scalable data layers combining distributed relational stores, analytical OLAP databases, and multi-tier DynamoDB and Redis caching.

Core Technologies & Tools

SQL ServerClickHouseDynamoDBMongoDBRedisVector Stores

Core Programming & Data Science

Writing clean, maintainable Python and SQL for heavy data transformation, automated schema validation, and statistical analysis.

Core Technologies & Tools

PythonSQLPandasNumPyData ValidationModel Evaluation

03 PORTFOLIO

Business solutions & measurable impact

Autonomous Sprint & Delivery Operations Agent

⚡ 5 hrs → 30 mins
In production
+
TOP IMPACTWeekly sprint checks cut from 5 hours down to 30 minutes
⚡ 90% Manual Overhead Eliminated
Architecture & Data Flow
CLIENTGATEWAY & AUTH★ AGENT CORETOOL PROTOCOLENTERPRISE SYSTEMSMS TeamsAdaptive Cards & ChatAzure Bot ServiceOAuth2 · User TokenLANGGRAPH ROUTERClaude Sonnet & OpusQuery Complexity RoutingMCP Server50+ Tool CallsServiceNow APIITSM Tickets & CMDBJira Software APISprints, Epics & HygieneMicrosoft GraphM365, Mail & CalendarBackground WorkerScheduled Sprint AuditsPostgreSQL StoreState & CheckpointerArize PhoenixOTel Spans & Observability

Problem

Sprint checks, ticket hygiene, leave tracking, and stakeholder reports were done by hand across ServiceNow, Jira, and Microsoft Graph.

Solution

A LangGraph agent dynamically routing between Claude Sonnet and Opus based on query complexity, with 50+ MCP tools running sprint and hygiene workflows traced in Phoenix.

Key Impact

5 hrs → 30 mins

Weekly sprint work that took about 5 hours now takes about 30 minutes (90% manual effort saved).

Stack:Agentic AILangGraphSonnet & OpusAzure AI FoundryMCPPhoenix

Enterprise Knowledge Modernization & Migration Engine

⚡ 2,000+ Human Hrs Saved
Align Technology·In production
+
TOP IMPACTSaved 2,000+ human hours of converting PDF & document text to HTML and article creation
⚡ 500+ Human Hrs Saved monthly
Architecture & Data Flow
CATALOG INPUTBATCH QUEUESHAREPOINT REPOSINGLE AZURE FUNCTION★ AZURE AI FOUNDRYSERVICENOW KBDOCXPDFASPXPDF StreamTextCodeHTMLServiceNow CatalogSubmit Batch Excel (.xlsx)Self-Service PortalRow-by-Row ParserProcesses 1 by 1Atomic State TrackingSharePoint SyncFetch Doc & ClassifyDOCPDFASPXSINGLE AZURE FUNCTIONDOCX HandlerSharePoint Direct PDF ExportPDF Text & ImagesExtract Text · Decouple ImagesASPX Asset SeparatorSeparate Assets & Raw Code★ AZURE AI FOUNDRYLLM HTML ConversionPDF Text & ASPX Code → HTMLSemantic Tags & Assets LinkedServiceNow KBREST API IngestionLive Article Created

Problem

Business users needed to batch-migrate legacy enterprise articles (PDF, ASPX, DOCX) from SharePoint into ServiceNow KB without manual copy-pasting.

Solution

Automated ServiceNow catalog ingestion parsing Excel rows 1-by-1, executing a unified Azure Function for multi-format extraction (DOCX PDF export, PDF text/image parsing, ASPX asset separation), utilizing Azure AI Foundry LLMs for semantic HTML conversion of both PDF and ASPX articles.

Key Impact

2,000+ Human Hrs Saved

Saved 2,000+ human hours of converting PDF & ASPX text to HTML and creating articles in ServiceNow.

Stack:ServiceNowAzure FunctionAzure AI FoundrySharePointPython

Intelligent Workforce Helpdesk & Support Resolution Agent

Align Technology·In production
+

Problem

Employees spent excessive time searching through 2,000+ fragmented KB articles, navigating complex catalog ordering, and tracking ticket updates across portal silos.

Solution

An AI agent in MS Teams offering conversational Q&A over 2,000+ articles, in-chat catalog item requests, real-time ticket push notifications, and adaptive role-based views for Individual Contributors and Managers.

Key Impact

50% Faster Resolution

Cut query resolution time by 50% with instant conversational answers and proactive ticket status updates directly in Teams.

Stack:ServiceNowMS TeamsAzure AI FoundryRAGVector SearchPython

Automated Talent Acquisition & Referral Screening Pipeline

Align Technology·Shipped
+

Problem

Referrals needed resume reading, skill sorting, acknowledgements, and status tracking by hand.

Solution

Microsoft Forms and SharePoint feed an LLM that parses resumes, classifies skills, sends acknowledgements, and tracks status.

Key Impact

Form to tracked status

A referral moves from the form to a tracked status without a manual handoff.

Stack:LLMMicrosoft FormsSharePoint

High-Throughput Real-Time Event Streaming & Analytics Platform

Locobuzz·Shipped
+

Problem

Batch processing could not keep up with thousands of concurrent users.

Solution

A Kafka pipeline handles ingestion, transformation, and failures. Airflow runs scheduled and on-demand jobs.

Key Impact

80% less processing time

Processing time dropped by 80%. Reporting latency dropped by 80%.

Stack:KafkaPythonAirflowDynamoDB

04 Education

Education

BTech in Computer Science

SRM Institute of Science and Technology

Sept 2020 - May 2024 · GPA 9.2 / 10.0

05 Offer

How I can help

AI Agent Development+

Production-ready agents and multi-agent workflows for real business processes.

MCP & Tool Integrations+

MCP servers connecting agents to enterprise APIs, databases and business tools.

LLM & RAG Systems+

RAG pipelines, semantic search, embeddings, knowledge ingestion and context-aware apps.

Backend & API Development+

Python/FastAPI microservices, REST APIs, async and event-driven systems.

AI Workflow Automation+

Document processing, knowledge management, enterprise workflows and repetitive ops.

AI System Architecture+

Reliable agentic systems with clear boundaries, tool orchestration and failure handling.

Production AI Engineering+

Phoenix tracing for agent runs, tool calls, and LLM spans, plus testing and deployment.

Get in Touch

06 Contact

Get in touch

Open to AI and backend roles, and to agent projects.

Location

Hyderabad, India

What should I call you?

Where I can reply.

At least 10 characters.