Vidhya Sagar

Curriculum vitae

Kokirala Vidhya Sagar

AI Engineer / Hyderabad, India

Download CV vidhyasagar54321@gmail.com

Summary

Four years building production Generative AI systems — RAG chatbots, multi-agent workflows, LLM evaluation pipelines and the APIs around them. Much of that work has shipped inside customer environments rather than on hosted infrastructure, which shapes how I choose models: the largest one is rarely the deployable one. Microsoft Certified Azure AI Engineer Associate.

Experience

  1. AI Engineer

    Red NucleusMar 2026 — PresentCurrent

    6
    AI products in production
    3
    evaluation layers: RAGAS, DeepEval, Langfuse

    Production AI products for Learning Management System platforms — retrieval, evaluation and agentic personas, shipped to multiple clients.

    What I owned

    • RAG chatbotAnswers user questions from source documents, running across multiple client LMS platforms.
    • LLM evaluationSynthetic test datasets with RAGAS and automated response scoring with DeepEval, so quality is checked the same way before every release.
    • Answer evaluatorAssesses learner answers inside LMS modules automatically, in use by multiple clients.
    • Agentic personasPersistent, stateful persona context on Letta, driving interactive simulations across scenarios.
    • Text-to-SQLPlain-English questions against the admin database, exported as shareable files — self-service data for managers.
    • ObservabilityLangfuse tracing across every AI product: prompts, retrieval steps and model responses, end to end.
    • Python
    • FastAPI
    • Azure OpenAI
    • Letta
    • RAGAS
    • DeepEval
    • Langfuse

  2. Senior Consultant — AI Engineering

    Atos SyntelAug 2022 — Mar 2026

    70%
    less manual test design effort
    80%
    of validation tasks automated
    8
    agents in production

    Two Gen AI platforms for enterprise quality engineering, shipped into customer environments — including air-gapped ones where a hosted model API was never an option.

    What I owned

    • On-prem model deploymentQwen, Llama and Gemma on customer-secured VMs, fine-tuned with LoRA and QLoRA to fit their hardware.
    • Multi-agent architectureLangGraph, round-robin and role-based patterns, per-agent memory with state in Redis and vector stores.
    • Backend and APIsDjango for orchestration, FastAPI and Flask for the model-facing layer.
    • Enterprise integrationJIRA, ALM, Rally and Azure Boards for requirement sync and defect traceability.
    • DeploymentContainerised and shipped across customer environments that resembled each other very little.

    Platforms built

    QE AssistGen AI platform automating test case creation, requirement analysis, test data mining and defect traceability.
    • Multi-provider model layerAzure OpenAI, AWS Bedrock, GCP Gemini or self-hosted Llama — selected per customer, because not every environment permits an outbound call.
    • Test data miningStructured extraction from documents, logs and APIs, with synthetic generation where real data was missing.
    • Prompt engineeringDomain-specific prompting and low-code automation for QA teams who do not write Python.
    QE AgentsMulti-agent framework where independent agents collaborate on QA and DevOps workflows.
    • Test case generationFrom PRDs, user stories and JIRA tickets, covering UI, API, SAP and Salesforce flows.
    • Ambiguity detectionFlags missing, unclear or conflicting requirements before they reach test planning.
    • Code and PR reviewReads Git repositories for vulnerabilities and bad patterns, proposing diff-based fixes.
    • Defect validationChecks reproduction steps and logs against system behaviour, cutting invalid submissions.
    • Python
    • LangGraph
    • Django
    • FastAPI
    • Docker
    • LoRA / QLoRA
    • Azure · AWS

Technical skills

Generative AI
  • RAG
  • Prompt engineering
  • Text-to-SQL
  • LLM evaluation
  • Fine-tuning (LoRA, QLoRA)
Agentic AI
  • LangGraph
  • Letta
  • Multi-agent systems
  • Agent memory & state
LLMs & platforms
  • Azure OpenAI
  • AWS Bedrock
  • Google Gemini
  • Llama
  • Qwen
  • Gemma
  • Hugging Face
Eval & observability
  • RAGAS
  • DeepEval
  • Langfuse
Languages
  • Python
  • SQL
  • JavaScript
  • HTML
  • CSS
Frameworks
  • FastAPI
  • Django
  • Flask
  • REST APIs
Data & infra
  • PostgreSQL
  • Redis
  • Vector databases
  • Docker
  • Git
  • CI/CD
  • Linux
  • Azure
  • AWS

Certifications

Microsoft Azure
  • AZ-900 Fundamentals
  • AI-900 AI Fundamentals
  • AI-102 AI Engineer Associate
Amazon Web Services
  • Cloud Practitioner
  • AI Practitioner

Recognition

  • Employee of the Quarter
  • Innovation Award
  • Spot Recognition — test automation
  • Spot Recognition — solution delivery
  • Customer Demo Recognition

Education

  • MCA, Computer Science University of Madras 75.7%
  • BSc, Computer Science AP Residential Degree College 62%
Get in touch See the projects