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Laxmi Shravani Mamidala

Generative AI Engineer • Dallas, TX, USA • s****************@gmail.com • +14******114 • linkedin.com/••••• • drivetube.ai/•••••

Professional Summary

Generative AI Engineer with 5+ years of experience designing and delivering production-ready LLM agents, RAG pipelines, and MLOps solutions. Skilled in building scalable inference APIs, multi-agent workflows, evaluation and benchmarking pipelines, and developer tooling across the SDLC. Experienced mentoring engineers, authoring technical specifications, and driving adoption of reusable AI components in academic and enterprise environments.

Technical Skills

Programming Languages: Python,Java,R,JavaScript
Web Technologies: HTML,CSS,REST APIs
Frameworks and Libraries: LangChain,FastAPI,Flask
Databases: SQL
Cloud and DevOps: AWS,Azure,GCP,Cloud Architecture,Docker,Kubernetes,MLflow,CI,CD,Model Deployment,Monitoring,Scalability,Reliability,Automated Testing,TDD,Observability
Data and Analytics: Machine Learning,Deep Learning,NLP,Transformers,Predictive Modeling,Classification,Regression,Reinforcement Learning,DQN,Feature Engineering,Statistical Analysis,Model Evaluation,Benchmarking,Data Generation,ETL
Tools and Methodologies: Git,SHAP,LIME,Grad-CAM,Ontology,Data Modeling,Dash,Plotly,Jupyter,JIRA,Agile
Skills: Unix,Command Line,Algorithms,Data Structures,Abstraction
Generative AI and LLMs: Large Language Models,GPT,Gemini,LLaMA,Prompt Engineering,Retrieval-Augmented Generation,Embeddings,Vector Search,FAISS,LangGraph,LangSmith,CrewAI,Tool Calling,Function Calling,Structured Outputs,Context Management

Work Experience

University of North Texas
Denton, TX
AI Engineer
May 2025 – May 2026
Built generative AI systems, multi-agent workflows, and developer-facing AI tooling in a university research and engineering environment supporting academic and institutional applications.
Tech Stack: LangGraph, CrewAI, LangChain, FastAPI, Docker, FAISS, Vector Search, CI, CD, TDD, Monitoring
  • Led design and delivery of multi-agent LLM applications using LangGraph and CrewAI to automate complex engineering workflows and provide reusable agent templates and skill configurations for developer enablement.
  • Architected scalable AI web services and real-time inference APIs using FastAPI, containerized with Docker and deployed to cloud; implemented CI/CD and TDD to improve release reliability and reduce regressions.
  • Developed LLM evaluation and benchmarking pipelines for retrieval quality and inference performance; integrated RAG components, vector search, and FAISS to support production-grade retrieval workflows.
  • Authored technical specifications, ontology-aware retrieval designs, workflow documentation, and reference implementations to standardize RAG components and accelerate cross-team adoption.
  • Provided mentorship and senior technical enablement on prompt engineering, RAG implementation, model evaluation, and repository-level AI tooling; led code reviews and technical sessions to raise engineering standards.
  • Implemented monitoring, observability, and model performance checks for AI services, establishing logging and alerting to detect drift and maintain service availability in production.
University of North Texas
Denton, TX
Data Scientist
August 2024 – May 2025
Built end-to-end ML pipelines and deployed models to APIs and dashboards to support university analytics and research projects.
Tech Stack: Python, TensorFlow, SQL, FastAPI, SHAP, LIME, Dash, Plotly, MLflow, CI, CD
  • Developed end-to-end ML pipelines using Python and TensorFlow, owning requirements through model deployment, monitoring, and SDLC execution; integrated models into scalable REST APIs.
  • Processed and prepared 10,000+ records for predictive modeling and text analytics; executed feature engineering and statistical workflows to improve model precision and F1-score.
  • Applied SHAP and LIME explainability to produce interpretable model insights for stakeholders, informing feature selection and governance decisions.
  • Implemented model versioning, automated tests, and CI/CD to ensure reproducible experiments and reliable model rollouts into production.
  • Built dashboards and visualizations with Dash and Plotly to communicate model outcomes and actionable insights to non-technical leadership.
  • Collaborated cross-functionally to clarify technical trade-offs, document experiments, and create reproducible pipelines for future research and operational use.
Infosys Limited
Hyderabad, India
Technology Analyst
June 2018 – July 2023
Delivered enterprise AI and software engineering solutions for large-scale digital platforms across consulting engagements, working across requirements, architecture, build, test and delivery.
Tech Stack: Python, ETL, Microservices, Docker, CI, CD, MLflow, Automation, JIRA, Agile
  • Architected and implemented production microservices and ETL pipelines in Python to support large-scale digital platforms; contributed to principal-level design reviews and system architecture.
  • Led design and adoption of the IDSML platform to automate AI workflows, experimentation, and benchmarking, enabling consistent internal tooling across engineering teams.
  • Implemented code-generation utilities and automation to reduce repetitive manual coding, improving developer productivity and accelerating delivery cycles.
  • Automated enterprise Python-based workflows across 300+ network devices to improve troubleshooting speed, repeatability, and operational reliability.
  • Provided technical leadership as Scrum Master and technical mentor; conducted code reviews, hands-on pairing, and enforced SDLC best practices to improve engineering standards and secure data handling.
  • Delivered performance and maintainability improvements that reduced maintenance overhead and increased service performance by 30% through refactoring and architecture changes.

Projects

ProComp: Multi-Agent LLM Framework for Scholarly Evaluation | May 2025 – May 2026
Tools Used: RAG, LangGraph, Role-based Agents, Structured Outputs, Embeddings, Vector Search, Python
  • Built a multi-agent LLM evaluation system that simulates peer review using RAG, role-based agents, and structured outputs to automate scholarly analysis across 190+ papers.
  • Implemented explainable scoring and ranking algorithms to ensure reliability and consistency in automated evaluations and to support human-in-the-loop review.
Agentic AI for Enterprise Conversation Analysis and Workflow Automation | May 2025 – May 2025
Tools Used: CrewAI, Transcription, Metadata Extraction, REST APIs, Python
  • Developed a CrewAI-based pipeline for enterprise conversation intelligence, integrating transcription, metadata extraction, and REST APIs for downstream analytics.
  • Designed automated agent workflows to transform raw conversation data into structured insights and actionable tasks for enterprise workflows.
Real-Time Object Detection and Deep Learning Inference System | August 2025 – August 2025
Tools Used: YOLO, Flask, FastAPI, Grad-CAM, Computer Vision, Model Optimization
  • Built a YOLO-based object detection pipeline with Flask/FastAPI deployment and real-time inference performance tuning.
  • Optimized deep learning models and applied Grad-CAM for explainability and architectural guidance in computer vision tasks.
Deep Reinforcement Learning for Decision Optimization using DQN | January 2025 – January 2025
Tools Used: Deep Q-Network, Reinforcement Learning, Neural Networks, Python
  • Implemented a DQN model with neural networks and reward optimization, achieving a 35% improvement over baseline strategies in decision-making tasks.
  • Analyzed convergence and exploration trade-offs to optimize policy performance in ambiguous environments.

Education

University of North Texas
Master of Science in Artificial Intelligence • Denton, TX • August 2024 – May 2026
Sri Indu College of Engineering and Technology
Bachelor of Technology in Computer Science Engineering • Hyderabad, India • August 2014 – May 2018

Certifications

Udacity AWS AI Practitioner Challenge — Udacity
Infosys Certified Python Associate — Infosys

Achievements

  • Distinguished MS Scholar Award: Awarded by University of North Texas for academic distinction in the MS AI program.
  • 2x Academic Excellence Award: Recognized twice for academic excellence during undergraduate studies.

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