SOFTWARE ENGINEER

Indira Devi Galla

Bellevue, WA **************@*****.*** +1-330-***-**** github.com/indira654 linkedin.com/in/indira-devi-galla-2851261a3

Professional Summary

I am a Software Engineer with experience in Data Science, Generative AI, and Machine Learning, boasting around 5 years of experience in designing, fine-tuning, and deploying AI/ML systems at enterprise scale. My expertise spans predictive modeling, vehicle pricing/volume forecasting, advanced analytics, and LLM-based innovation, where I have built solutions that integrate seamlessly with APIs, telecom platforms, and cloud infrastructures across AWS, Azure, and GCP. I am skilled in Python, PyTorch, TensorFlow, and Hugging Face. I specialize in transforming advanced models into production-ready systems that are scalable, efficient, and cost-effective. I have strong MLOps experience, leveraging Docker, Kubernetes, CI/CD, and MLflow to streamline deployment and monitoring. Beyond technical delivery, I prioritize data privacy and ethical AI, ensuring compliance with regulations such as GDPR and CPNI. I have also applied vector databases, attribution modeling, and knowledge graphs to create intelligent retrieval and decision-support systems. Passionate about forecasting, optimization, and data storytelling, I thrive at the intersection of AI innovation, business strategy, and enterprise impact.

Technical Skills

Programming & Frameworks: Python, Java, SQL, JavaScript, TypeScript, C#, C++, HTML, REST APIs, Spring Boot, OOP, Object-Oriented Design, Django, SPA, PWA, SPA, Golang.

Machine Learning & GenAI: Keras, PySpark, Hadoop, scikit-learn, XGBoost, Predictive Modeling, Optimization Algorithms, Experimental Design, Causal Inference, NLTK, Matplotlib, PyTorch, LSTM, Sentiment Analysis, Machine Learning Algorithms, Agentic AI, TensorFlow, GPT, LLaMA, LangChain, Hugging Face, OpenAI APIs, Prompt Engineering, RAG, Transformers, FAISS, Pinecone, Sentence Transformers, Claude, Amazon Titan, CNN, RNN.

Cloud & Data: AWS (S3, SNS, CloudWatch, Bedrock, Lambda, API Gateway, DynamoDB, SageMaker, CDK, Cloud-native Development, AWS ECS), GCP (BigQuery, Dataflow), Ray, Azure, Databricks, Snowflake, Kafka, SQS, Big Data Ecosystem, Oracle RDBMS, MongoDB, SQL Server, Azure Monitor, MySQL, Vertex AI, Data Flow, Data Proc, Redshift, BigQuery, Relational Databases, RDF, Monitoring & Reliability (SLO/SLI, error budgets, Prometheus, Grafana, CloudWatch, Stackdriver), Scale Testing, Data Dog, splunk.

Tools & Infra: Microsoft Excel, Jira, Docker, Kubernetes, JUnit, ServiceNow, Visual Studio, Scrum, Data Modeling, Power BI, Canva, Tableau, Tabular Editor, Agile Methodology, Airflow, GitLab, SSRS, ETL Tools, dbt, API, Code Review, System Design, Unit Testing, MVVM, Microservices Architecture, Continuous Deployment, Latency Reduction, Scalability, Load Balancing, DevOps Tools, CDM, Plotly, Dash, DAX Studio, JSON, XML, SOAP, MLflow, root cause analysis (RCA), Unit Testing, Functional Testing, Test Strategies, Automation Frameworks, Code Coverage, Terraform.

Systems & Foundations: Linux, Unix, Bash scripting, Algorithms, Data Structures (arrays, linked lists, trees, graphs), Operating System Concepts, Scalable Backend Systems, Design Patterns.

Work Experience

Software Engineer – Generative AI June 2024 – May 2025

Caliber IT Solutions, Rockhill, SC

•Build intuitive, user-friendly interfaces that make interacting with large language models (LLMs) and AI agents simple and effective.

•Defined product requirements and developed predictive modeling features using scikit-learn and XGBoost, aligning delivery with the project roadmap to support telecom analytics use cases. Design and deploy generative AI solutions using AWS Bedrock (Claude, Titan), focused on telecom needs like customer support and network diagnostics.

•Developed and analyzed vehicle pricing/volume forecast scenarios, providing actionable recommendations for strategic planning .Conducted A/B testing and experiments to evaluate product performance, applying causal inference techniques.

•Developed scalable, secure cloud-native systems using AWS services such as Lambda, SageMaker, DynamoDB, and S3.

•Optimize prompt engineering and model orchestration to improve performance and reduce costs, incorporating telemetry and logging to monitor system behavior. Implement infrastructure-as-code with Terraform and AWS CDK to ensure consistent and reliable deployments.

•Created compelling dashboards and data stories using Tableau, Power BI, and Plotly to influence partner decision-making. Operated and monitored ML tuning and inference endpoints on Ray, troubleshooting performance issues and optimizing customer-facing workloads. Developed and deployed ML pipelines on GCP (BigQuery, Dataflow, Vertex AI) for large-scale data ingestion, preprocessing, and production deployment.

•Applied object-oriented design, data structures, and complexity analysis to optimize AI pipelines. Built query systems for high-performance search and retrieval.

•Orchestrated data pipelines with Airflow (DAGs for scheduling, monitoring, error handling) improving pipeline reliability and automation. Contributed regularly to Agile sprints by planning, reviewing code, testing features, and improving build pipelines.

Software Engineer – Data Scientist & ML Systems Feb 2020 - July 2023

Nextgen IT Tech, Hyd, India

•Built NLP models for classification, sentiment analysis, and topic modeling using spaCy and NLTK.

•Designed and executed experiments to validate features, applying experimental design best practices.

•Applied PCA and t-SNE to analyze complex datasets and improve visualization.

•Fine-tuned LLMs for an internal chatbot using domain-specific data.

•Developed real-time prediction systems by integrating ML models with distributed pipelines.

•Applied optimization models for resource allocation and efficiency improvements in large-scale systems. Created analytics dashboards in Tableau and Power BI for actionable insights.

•Built forecasting models (Prophet, LSTM) and anomaly detection algorithms to predict trends and identify irregular patterns in datasets.

•Automated deployments with Docker, Jenkins, and CI/CD pipelines, ensuring scalability.

•Maintained detailed documentation for reproducibility and compliance.

Projects

LLM-Powered FAQ Bot

An AI chatbot built with OpenAI, LangChain, FAISS, and Streamlit securely answers internal company questions based on document embeddings, reducing new hire onboarding time by 40%. This enhances efficiency and information access by streamlining searches through company knowledge bases.

Database Systems Exploration: From SQL to MongoDB

Developed a full-stack mini database system using Python, Peewee ORM, SQLite, and MongoDB, implementing core CRUD operations. Followed agile practices and performed thorough testing to ensure scalability, data abstraction, and data integrity across platforms.

Kent Student Centre Stalls and their Most Special Items

Developed a modular Java food ordering platform featuring a custom SQLite database, Git version control, and DevOps practices. This system features real-time user tracking and leverages IBM Design Thinking to deliver an enhanced user experience.

ETL Pipeline Optimization & ML Model Deployment

Created Python data pipelines to clean data for Scikit-learn and PyTorch models, then deployed classification and forecasting models with automated testing, logging, and monitoring. Built real-time dashboards using Power BI and Google Data Studio.

Interactive Volume Rendering: X-Ray and MIP Visualization with Ray Casting

Developed an interactive 3D volume rendering tool using ray casting to visualize medical datasets with X-Ray and Maximum Intensity Projection techniques. The tool features an FLTK-based graphical user interface (GUI) that allows users to load, rotate, and scale the visualizations in real-time, showcasing its practical application in medical imaging.

Key Achievements

•Built reliable, scalable analytics tools focused on customers to help teams make smarter decisions.

•Enhanced data accuracy from 30% to over 90%, enabling reliable AI model training and smarter telecom decision-making. Utilized nonlinear optimization techniques to fine-tune AI models for higher accuracy.

•Built scalable analytics and AI tools that support telecom teams in customer support automation and network diagnostics. Applied linear programming to optimize telecom resource allocation.

Education

Masters in Data Science, Kent State University, Kent, OH May 2025

Bachelors in Computer Science, Little Flower Degree College, India July- 2021

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