Vijay M.

Key Skills

Software

Don't disclose

Top Subject Matter

AI-assisted contact center intelligence (bots, IVR assurance, agent assist)

AI-assisted call failure investigation and log analysis

Legal Services & Contract Review

Top Data Types

Text

Image

Document

Top Task Types

Prompt + Response Writing (SFT)

Question Answering

Freelancer Overview

AI-Assisted Development & AI-Native Contact Center Product Innovation (personal portfolio). Brings 22+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Don't disclose, Internal, and Proprietary Tooling. Education includes Bachelor of Science, University of Arkansas (2005). AI-training focus includes data types such as Computer Code, Programming, and Text and labeling workflows including Prompt + Response Writing (SFT) and Question Answering.

Labeling Experience

Sr Genesys Architect — built Splunk/AWS Connect analytics feeding AI investigation workflow

TextQuestion Answering

Built analytics and troubleshooting experiences that answer investigation questions about contact-center failures by correlating end-to-end event/log signals. Engineered architectures that convert multi-system traces into call-journey explanations suitable for rapid operational decision-making. Designed parsers and query pipelines intended to be vendor-agnostic across multiple CCaaS environments. • Correlated logs across Cisco UCCE components into call journeys • Created plain-English “why did this call fail?” responses • Integrated MCP to route AI queries into Splunk/JIRA/Confluence/AppDynamics • Implemented on-prem deployment options with LDAP/AD auth and HIPAA/PCI-DSS protections

2020 - 2026

AI-Assisted Development & AI-Native Contact Center Product Innovation (personal portfolio)

Don't disclosePrompt + Response Writing (SFT)

Developed AI-assisted and rules-driven conversational capabilities and testing workflows in contact-center architectures, emphasizing scenario validation and behavior checks for bot/IVR components. Built integration patterns that translate plain-English intents into executable behaviors and evaluation outputs. Adapted systems for multiple tenants/environments with secure handling of sensitive data. • Claude Code/Claude API workflows for AI-assisted product development • Ollama on-prem fallback for local execution • Amazon Bedrock runtime for AI showcases • Docker-deployable pipelines for repeatable demo and evaluation

2005

Education

Work History