Software Engineer Real-Time

Hemanth Reddy

484-***-**** ***************@*****.*** www.linkedin.com/in/hmaram

Summary

Results driven Software Engineer with a proven track record of designing and delivering high-performance, scalable systems across cloud-native, AI/ML, and distributed environments. Specialized in building end-to-end solutions using AWS, Python and Java with hands on expertise in Generative AI (Bedrock, SageMaker, Hugging Face), real-time data pipelines (Kafka, Kinesis), and microservice architectures. Demonstrated success in reducing system latency by up to 60%, automating infrastructure provisioning across multi-account AWS environments, and deploying secure, production ready applications that support millions of users. Adept in leveraging CI/CD, Docker, Kubernetes, and Terraform to enable rapid, reliable releases. Passionate about innovation, security, and translating complex technical challenges into impactful real-world solutions. Technical Skills

Programming & Scripting: Python, Java, C++, C#, JavaScript, TypeScript, Bash Scripting. Web Technologies: Node.js, Rest API, FastAPI, Django, Spring, Flask, JSP, JDBC. AI/ML & Data Science: LLM, PyTorch, TensorFlow, Hugging Face Transformers, Pandas, NumPy, SciPy, OpenCV, Keras. Cloud & DevOps: AWS (Lambda, S3, API Gateway, SageMaker, Bedrock, Cloudformations, CloudWatch, IAM, Fargate, VPC), Docker, CI/CD, GitLab, Kubernetes, Terraform, IAC, Jenkins. Networking & Protocols: TCP/IP, DNS, HTTP/HTTPS, SSL/TLS, SSH, OAuth. Databases: MySQL, NoSQL (DynamoDB, MongoDB), PostgreSQL, Redshift. Data Processing & Streaming: AWS Glue, PySpark, Apache Kafka, AWS SQS/SNS, AWS Kinesis. Testing: Pytest, Unittest, Junit, Mockito, Load testing, Canary testing, Integration testing. Additional Skills: Unix, SAAS, VS code, Jupyter Notebook, Excel, PowerBI. Experience

Software Development Engineer, Amazon Web Services July 2022 – Present

Migrated legacy infrastructure to AWS CDK, architecting multi region stacks with integrated metrics and alarms to ensure high availability and proactive monitoring, which led to a 35% reduction in downtime incidents.

Developed automation scripts using Python's boto3 library to programmatically manage AWS resources. This includes automating the provisioning and configuration of Lambda functions, CloudFormation stacks, and S3 buckets, which reduced manual configuration errors and accelerated deployment cycles.

Developed and fine-tuned a Generative AI chatbot using Amazon Bedrock by leveraging foundational models

(Amazon Titan) and implementing Retrieval Augmented Generation (RAG) to connect with team’s internal knowledge bases, enabling contextual, real-time responses specific to team’s data.

Designed and implemented serverless Spark ETL pipelines on AWS Glue to process over 10 TB of raw data daily from S3, transforming it into optimized Parquet format and loading it into Amazon Redshift. Achieved a 60% improvement in query performance and reduced storage costs by 40%, enabling efficient, large-scale analytics.

Implemented a real-time recommendation engine using Amazon SageMaker and Kinesis Data Streams, increasing user engagement by 25% through personalized, dynamic content delivery.

Developed, trained, and deployed ML models using Amazon SageMaker, reducing end-to-end model lifecycle time by 60% through automated hyperparameter tuning, evaluation, and deployment pipelines.

Fine-tuned custom NLP models using Hugging Face Transformers, and deployed them as scalable, serverless endpoints using Lambda and API Gateway, enabling real-time inference with minimal infrastructure overhead.

Scheduled AWS Fargate tasks to automate periodic operations and streamline workflows, improving operational efficiency and reducing costs by 20%.

Built and maintained CloudFormation templates for multi-account infrastructure provisioning, reducing environment setup time by 80% and ensuring consistent, repeatable deployments.

Enhanced security compliance by collaborating with security engineering teams to refine vulnerability detection mechanisms, which improved remediation time by 25% for out of SLA hosts.

Implemented a comprehensive testing suite for Python based applications using PyTest and unittest, including unit, integration, and canary tests, increasing test coverage by 40%.

Containerized microservices using Docker and deployed them on Kubernetes clusters (EKS), implementing Horizontal Pod Autoscaling to ensure 99.9% uptime under dynamic workloads. Graduate Research Assistant, Villanova University Sep 2021 – June 2022

Assisted in teaching, developing related course work, and evaluating undergrad students for computer system course.

Provided academic guidance and conducted one-on-one mentoring sessions with both undergraduate and graduate students, addressing questions on assignments, research and relevant course work.

Gathered and analyzed student performance data to identify trends, producing reports that guided targeted interventions and curriculum improvements.

Software Engineer, Cognizant Jan 2020 – Dec 2020

Developed and deployed RESTful APIs using Flask and FastAPI to power 6 microservices, improving response time by 40% and enabling secure data exchange across a distributed system serving 50K+ users.

Implemented the OAuth 2.0 authorization framework to secure REST APIs, managing authentication flows, access tokens, and enhancing user data protection across distributed services.

Built a scalable real-time data pipeline using Apache Kafka to stream and process high-volume transactions, improving system reliability by 30% and enabling low-latency, fault-tolerant message delivery across distributed Systems.

Developed an automated CI/CD pipeline using Jenkins and Terraform, reducing deployment time by 60% and eliminating 95% of manual errors in infrastructure provisioning. Enabled seamless, repeatable deployments, resulting in 30% faster release cycles and enhanced developer efficiency.

Developed and maintained a Django based content management system, improving content publishing efficiency by 30% and driving a 15% increase in web traffic. Optimized database performance using select_related and prefetch_related, reducing query execution time and boosting API efficiency by 40%.

Developed a high performance, scalable web application using Spring Web Flux’s reactive stack, enabling non-blocking, asynchronous request handling and improving system responsiveness by 50% under high concurrency, supporting up to 5000 concurrent users with minimal latency.

Implemented JWT-based authentication and authorization using Node.js, ensuring secure access control and protecting APIs against unauthorized access.

Used PostgreSQL for writing and executing complex SQL queries to store and retrieve data via JDBC in a Java- based application, reducing query response time by 35% and supporting seamless data flow for over a million records.

Projects

Data Protection System

Designed a secure data protection system using Python and PyCryptodome, encrypting confidential data with AES and decrypting it via user specific keys. Implemented IP whitelisting and an intrusion alert mechanism, enhancing system security by detecting and notifying unauthorized access attempts.

Used AWS Lambda to run Python scripts and maintained a centralized whitelist in DynamoDB to instantly verify source IP addresses during automated data processing.

Developed an automated alert system using Python’s smtplib and IP whitelisting logic to notify administrators via email when incoming source IPs did not match the approved list, enabling real-time intrusion detection.

Logged all decryption events, IP checks, and alert triggers using CloudWatch for auditing and monitoring, enabling rapid detection and investigation of unauthorized access attempts. Real Time Face and Vehicle Detection System

Developed a real-time face recognition and vehicle detection system using Python and OpenCV, enhancing security through intelligent surveillance capabilities.

Utilized Haar cascades for robust facial feature detection and tracking under varying lighting conditions and angles.

Integrated YOLO and Keras for accurate vehicle detection and license plate character recognition, improving segmentation precision and model reliability.

Deployed the solution on a Raspberry Pi Model B, minimizing hardware costs and power requirements without sacrificing core functionality.

Education

Master of Science, Computer Science, Villanova University Jan 2021 – Dec 2022 Coursework: Design and Analysis of Algorithms, Programming Languages, Software Project Management, Computer Systems, UNIX, Python with Data Science and Theory of Computability. Bachelor of Technology, Computer Science, GITAM University June 2016 - June 2020 Coursework: Object Oriented Software Development, Data Structures, Database Management Systems, Design and Analysis of Algorithms, Web Development, Compiler Design, Network security and Cryptography, UNIX, Artificial Intelligence and Machine Learning.

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