LOKESH NAIDU BAVIGADDA
Dallas,TX
Summary
Software Engineer with 4+ years building scalable distributed systems, AI-powered data platforms, and production ML pipelines at scale. Expert in designing low-latency microservices, real-time streaming architectures, and cloud-native solutions processing 50M+ daily events. Strong foundation in algorithms, system design, and infrastructure automation with proven ability to ship high-impact features across the full stack. Passionate about leveraging AI/ML to solve complex engineering challenges.
Overview
5
5
years of professional experience
1
1
Certification
Work History
Software Engineer, Data Engineering & AI
Brook Mays Music (Universal Melody Services LLC)
04.2024 - Current
- Architected and deployed enterprise Java-based data platform on Azure, collaborating with SRE and architecture teams to standardize infrastructure framework across 5+ business units, reducing deployment time by 40% and infrastructure costs by $250K annually
- Designed and built highly scalable, fault-tolerant data pipelines using Azure Data Factory and Apache Airflow, orchestrating 50M+ daily events with 99.9% SLA, supporting real-time analytics for $10M+ revenue operations
- Pioneered AI-powered operational intelligence system by integrating Azure OpenAI and custom LLM agents for automated log analysis, anomaly detection, and incident prediction, reducing MTTR by 60% and preventing 40+ P1 incidents proactively
- Engineered production-grade microservices in Java and Python with RESTful APIs, enabling real-time data synchronization across 15+ distributed services with p99 latency under 100ms and 99.95% availability
- Built event-driven architecture using Azure Service Bus and Kafka, processing 2M+ messages/day with exactly-once delivery guarantees, implementing dead-letter queues and retry mechanisms for fault tolerance
- Led Kubernetes adoption on AKS, containerizing 20+ applications with auto-scaling policies, implementing blue-green deployments and Canary releases, improving resource utilization by 35% and reducing infrastructure spend
- Established end-to-end CI/CD pipelines with JFrog Artifactory and GitHub Actions, implementing automated testing (unit, integration, E2E), reducing deployment cycles from weeks to hours with 99% success rate
- Developed Python automation framework with AI-powered decision engine for intelligent workflow orchestration, eliminating 85% of manual operations and reducing human error by 95%
- Implemented comprehensive security practices including OAuth 2.0, JWT authentication, encryption at rest/transit, Hashicorp Vault integration, achieving SOC 2 compliance and passing security audits
Graduate Research Assistant – ML & IoT Systems
University of North Texas
01.2023 - 12.2023
- Designed and implemented real-time IoT data ingestion pipeline using Azure IoT Hub, Stream Analytics, and Databricks, processing telemetry from 500+ sensors with 99.5% data accuracy and sub-second latency
- Built end-to-end ML pipeline for predictive maintenance using XGBoost and Random Forest algorithms, achieving 92% prediction accuracy and reducing equipment downtime by 28%, saving $50K in maintenance costs
- Developed real-time anomaly detection system using unsupervised learning (Isolation Forest, Autoencoders), identifying critical failures 15 minutes before occurrence with 88% precision
- Optimized distributed Spark jobs on containerized infrastructure, implementing partition strategies and caching mechanisms that improved processing throughput by 45% and reduced compute costs by 30%
- Created interactive dashboards using Plotly and Dash for real-time monitoring and visualization of ML model performance, enabling data-driven decision making for research team
Associate Software Engineer
Capgemini
01.2022 - 12.2022
- Engineered scalable ETL pipelines using AWS Glue, Apache Airflow, and PySpark, processing 100GB+ daily data from 10+ heterogeneous sources with data quality validation and automated reconciliation
- Architected data warehouse solution on Amazon Redshift with dimensional modeling (star schema), implementing incremental loading, partitioning strategies, and materialized views that improved query performance by 45%
- Developed high-throughput streaming pipeline using Apache Kafka and Spark Structured Streaming, processing 10M+ events/day with exactly-once semantics, powering real-time analytics dashboards
- Built microservices-based backend system in Java (Spring Boot) and Python (Flask), implementing RESTful APIs with OAuth 2.0, rate limiting, and circuit breaker patterns for resilience
- Containerized 15+ applications using Docker and deployed on Kubernetes cluster, implementing horizontal pod autoscaling based on CPU/memory metrics and achieving 20% performance improvement
- Established automated testing framework using PyTest, JUnit, Mockito, and Selenium integrated with Jenkins CI/CD, achieving 90% code coverage and reducing production bugs by 40%
- Optimized database queries and implemented caching strategies (Redis) reducing average response time from 2.5s to 400ms, supporting 3x increase in concurrent users
- Mentored 3 junior engineers on code quality, design patterns, and agile practices, conducting weekly code reviews and technical workshops
Python Developer
Wipro
01.2021 - 12.2021
- Developed full-stack web applications using Django, PostgreSQL, and React, implementing JWT-based authentication, role-based access control, and responsive UI serving 5000+ daily active users
- Created Python automation framework for data migration and ETL processes, reducing manual effort by 40% and saving 15+ engineering hours weekly
- Deployed cloud-native applications on AWS EC2 with S3 for object storage, implementing auto-scaling groups, Application Load Balancer, and CloudWatch monitoring for 99.9% availability
- Built RESTful APIs with Django REST Framework supporting JSON/XML serialization, pagination, filtering, and comprehensive API documentation using Swagger/OpenAPI
Education
Master of Science - Artificial Intelligence
University of North Texas
Denton, TX12.2024
Bachelor of Technology - Electronics & Communication Engineering
Presidency University
Bangalore, India07.2022
Skills
- Languages: Java, Python, SQL, JavaScript/TypeScript, Bash, PowerShell, Scala
- AI/ML & Data Science: Azure OpenAI, LLM Fine-tuning, Prompt Engineering, TensorFlow, PyTorch, Scikit-learn, MLflow, Feature Engineering, Model Optimization, A/B Testing
- Distributed Systems: Apache Kafka, Spark, Airflow, Flink, Event-driven Architecture, Message Queues, Pub/Sub, Stream Processing, Batch Processing
- Backend & APIs: Spring Boot, Django, Flask, FastAPI, Nodejs, REST APIs, GraphQL, gRPC, Microservices, Service Mesh
- Cloud & Infrastructure: AWS (S3, EC2, Lambda, Glue, Redshift, EMR), Azure (ADF, AKS, Synapse, Service Bus), GCP, Terraform, CloudFormation
- Containers & Orchestration: Kubernetes, Docker, Helm, ArgoCD, Service Discovery, Auto-scaling, Load Balancing
- Data Engineering: ETL/ELT Pipelines, Data Modeling, Databricks, Apache Spark, PySpark, Data Warehousing, Lake Formation, Delta Lake
- Databases: PostgreSQL, MySQL, MongoDB, Redis, Cosmos DB, DynamoDB, Cassandra, Redshift, Snowflake, SQL Server
- DevOps & CI/CD: Jenkins, GitHub Actions, GitLab CI, JFrog Artifactory, Docker Registry, Infrastructure-as-Code, Blue-Green Deployment
- Tools & Monitoring: Git, JIRA, Datadog, Prometheus, Grafana, ELK Stack, New Relic, PagerDuty
Certification
- AI Innovation Leader: First engineer to integrate LLM-powered automation in production, recognized for 60% reduction in incident response time and improving system reliability to 99.95%
- Infrastructure Excellence Award: Led standardization of Azure data platform framework adopted across organization, saving $250K annually in infrastructure costs
- Scale Achievement: Architected mission-critical pipelines processing 50M+ daily records with 99.9% SLA, supporting $10M+ in revenue operations
- Open-Source Contributor: Active contributor to Apache Airflow and Kubernetes projects with 5+ merged PRs focused on performance optimization
Languages
English
Full Professional
Spanish
Limited Working
Hindi
Native or Bilingual
Interests
- Photography
- Passionate about astrophysics; regularly read and explore new concepts in cosmology, black holes, and quantum physics
Personal Information
Title: Software Engineer | Data Engineering AI/ML Distributed Systems
Timeline
Software Engineer, Data Engineering & AI
Brook Mays Music (Universal Melody Services LLC)
04.2024 - Current
Graduate Research Assistant – ML & IoT Systems
University of North Texas
01.2023 - 12.2023
Associate Software Engineer
Capgemini
01.2022 - 12.2022
Python Developer
Wipro
01.2021 - 12.2021
Bachelor of Technology - Electronics & Communication Engineering
Presidency University
Master of Science - Artificial Intelligence
University of North Texas