Machine Learning Python Developer

Rajesh Mameda

Full Stack Python Developer

+1-201-***-**** *************@*****.*** linkedin.com/in/rajesh-dataengineer

Summary

Full Stack Python Developer with expertise in both front-end and back-end development, specializing in building efficient, scalable web applications. Skilled in developing full-stack solutions using Python, frameworks like Django and Flask, and integrating with various APIs and databases. Proficient in automating workflows, optimizing server-side processes, and creating dynamic, user-friendly front-end interfaces using HTML, CSS, and JavaScript. Experienced in building robust data pipelines, custom scripts, and deploying web applications on cloud platforms like Azure and AWS. Adept at collaborating with cross-functional teams to deliver data-driven insights and ensure seamless application performance in the energy and finance sectors.

Skills

Programming Languages: Python, Java script, SQL, Machine Learning, Node.js

Tools: Azure Data Factory, Azure Synapse Analytics, Hadoop, Hive, AWS Glue, Aws Lambda, Airflow, Power BI, Pandas, PySpark, TensorFlow, PyTorch, Django, Flask, Matplotlib, Tableau, Looker,Docker,Kubernets

ETL & Data Integration: ETL Process Development, Data Migration, Data Pipeline Management, Data Quality, CDC.

Databases: MySQL, PostgreSQL, Azure SQL Database, Snowflake, BigQuery

Cloud Platforms: Azure (Data Factory, Synapse Analytics), AWS (S3, Lambda, Glue, EC2, RDS, SQS), GCPData

Data Processing: Pandas, PySpark, Data Transformation, Data Cleansing, Palantir

Machine Learning: AI, Machine Learning, Natural Language Processing, Large Language Models

Development Practices: CI/CD

Version Control & Collaboration: Git, Bitbucket, JIRA, Agile Methodology

Work Experience

Engie TX

python developer Sep 2023 - Present

•Designed and developed full-stack web applications using Python Django for the back end and react for the front end, ensuring seamless interaction and enabling real-time data updates across the application.

•Built and implemented user authentication systems in Django, including login, registration, and role-based access control, integrating them with react components for a secure and smooth user experience.

•Developed dynamic, responsive user interfaces in react, utilizing components, services, directives, RxJS observables, and Angular Forms to enhance modularity and interactivity.

•Developed and deployed interactive data applications using Streamlit for rapid dashboarding and FastAPI for high-performance backend APIs, supporting both internal analytics and external integrations.

•Designed and implemented microservices architectures leveraging FastAPI, enabling scalable, decoupled services across distributed systems.

•Architected and deployed cloud-native applications using AWS services, including Lambda, API Gateway, ECS (Fargate), RDS, and S3, following best practices for high availability and security.

•Applied AWS Well-Architected Framework principles to design fault-tolerant, cost-optimized, and secure solutions that scale with user demand.

•Built and containerized Python-based microservices using Docker, deploying them on AWS ECS with CI/CD pipelines via AWS CodePipeline and GitHub Actions.

•Utilized AWS CloudFormation and Terraform to automate infrastructure provisioning, ensuring consistent environments across dev, staging, and production.

• Implemented Celery with Redis for background task execution in FastAPI applications, enabling asynchronous workflows for notification systems and data ingestion.

• Optimized performance of REST APIs by leveraging async/await patterns in FastAPI and using Pydantic models for fast validation and serialization.

•Designed secure, scalable APIs with OAuth2/JWT authentication and authorization, integrating them with client applications via API Gateway.

•Tuned database queries and schema designs for PostgreSQL and DynamoDB, improving data retrieval speed and write throughput for analytics-intensive workloads.

•Created logging, monitoring, and alerting solutions using AWS CloudWatch, enabling proactive issue detection and system health tracking.

•Applied software architecture patterns such as microservices, event-driven architecture, and layered architecture to design maintainable, modular systems.

•Conducted system design reviews, translated business requirements into scalable software solutions, and ensured alignment with cloud-native best practices.

•Championed Test-Driven Development (TDD) and wrote unit/integration tests using pytest, achieving high code coverage and preventing regressions in production.

•Collaborated cross-functionally with DevOps, product, and data teams to deliver full-stack, cloud-native applications with fast iteration cycles and strong production stability.

Barclays NJ

Python developer May 2022 - Jan 2023

•Developed and maintained dynamic full-stack web applications using Python frameworks like Django and Flask, building robust, scalable back-end systems and RESTful APIs to enable seamless communication between server and front-end interfaces.

•Developed high-performance RESTful APIs using FastAPI, leveraging asynchronous I/O for faster response times and seamless integration with front-end apps built in Angular and Streamlit.

•Built interactive, data-driven dashboards using Streamlit, enabling real-time financial analytics and operational KPIs with Python back-end services.

•Replaced legacy Flask APIs with FastAPI to improve performance by over 40%, reducing latency and enhancing support for asynchronous tasks and background jobs.

•Integrated FastAPI with OAuth2 and JWT for secure authentication, implementing RBAC to ensure secure access control in financial and internal analytics platforms.

•Built internal tools for front-office teams (PMs, Quants) using Streamlit, allowing dynamic visualization and exploration of market data from Bloomberg, ICE, and Moody’s.

• Leveraged Pydantic and FastAPI’s validation system to enforce strict data contracts and reduce runtime errors, improving API reliability across services.

•Used MLflow to track machine learning experiments, manage model lifecycles, and deploy models into production pipelines integrated with web applications for predictive capabilities.

•Implemented containerized applications using Docker and deployed on Amazon EKS (Elastic Kubernetes Service), ensuring scalability, high availability, and efficient management of microservices and back-end APIs.

•Integrated AWS services such as Elastic Beanstalk, RDS, S3, and CodePipeline for hosting, scaling, and managing full-stack applications, leveraging cloud-native solutions for improved performance and cost-efficiency.

•Created secure authentication systems using OAuth2 and JWT in Django, implementing role-based access control (RBAC) for users across the application and ensuring robust authorization mechanisms.

•Developed custom dashboards and admin panels using Django and Angular, enabling real-time data visualization of business KPIs and system health metrics.

•Set up and maintained CI/CD pipelines using Jenkins, GitLab CI, and AWS CodePipeline/CodeBuild, automating testing, build, and deployment processes for faster and more reliable release cycles.

•Demonstrated experience with Version Control using Git and GitHub, applying branching strategies for team collaboration and code integration in fast-paced development cycles.

•Conducted unit, integration, and end-to-end testing with pytest, Jest, and Karma, ensuring comprehensive test coverage and stable application functionality across different environments.

• Actively participated in Agile methodologies including sprint planning, daily stand-ups, code reviews, and retrospectives, fostering a collaborative environment for continuous improvement.

•Developed serverless, event-driven functions using AWS Lambda, automating back-end processes and enhancing application scalability and flexibility.

•Integrated third-party services (Stripe, Google Maps, Firebase) into web applications, extending functionality and enabling features like payment processing and geolocation tracking.

•Used Power BI to create interactive data visualization dashboards, enabling real-time analytics and business insights by connecting data from multiple sources.

•Strong knowledge of financial market data, including handling datasets from Bloomberg, S&P, Moody’s, and ICE, and utilizing them in models for investment decision-making and backtesting.

•Applied investment universe customization and backtesting techniques for refining financial models, ensuring accurate predictions and optimal portfolio management.

•Extensive experience in supporting front-office end-users (PMs, Quant) by providing technical solutions to their analytical needs, integrating tools and data pipelines to enhance decision-making capabilities.

•Hands-on experience with cloud platforms (AWS), using services like Amazon S3, Lambda, RDS, and CodeDeploy for cloud-native application deployment and automation.

•Proactive and ownership mentality, consistently identifying opportunities for improvement and ensuring high-quality deliverables in dynamic, fast-moving environments.

•Solid fixed-income knowledge and ability to apply it in real-world data and algorithmic models to support financial decision-making processes.

•Demonstrated strong analytical and problem-solving skills across multiple projects, focusing on optimizing system performance and handling complex data challenges efficiently

Synergy Technologies, India June 2017-July2021

Python developer

•Led the design phase and data modeling for a healthcare data analytics platform, collaborating with cross-functional teams to define project requirements, project scope, and data processing workflows.

•Developed a dynamic data parsing tool using Python, HTML, CSS, and JavaScript that streamlined the processing of medical sensor data heart rate monitors, wearable fitness trackers, ECG devices, reducing processing time and automating previously manual data cleansing tasks.

•Integrated healthcare sensor data from IoT devices into the platform, enabling real-time data analysis for patient monitoring, improving decision-making, and reducing manual data entry errors.

•Developed machine learning models with Python libraries such as scikit-learn and TensorFlow to predict patient health outcomes (early detection of diseases such as diabetes, cardiovascular issues, etc.) by analyzing sensor data and medical history.

•Implemented a computer vision (CV) module using OpenCV and TensorFlow to analyze medical images, such as X-rays, CT scans, and MRIs, enabling automated diagnosis assistance and image segmentation for identifying anomalies in healthcare images.

•Designed and developed presentation tiers using Django templates, JavaScript, HTML, and CSS for visualizing patient data, sensor metrics, and diagnostic results in user-friendly interfaces, with built-in error handling and validation.

•Led front-end development using React.js and Bootstrap, creating intuitive interfaces for healthcare providers to manage patient data, access real-time health statistics, and track treatment progress.

•Developed cross-browser and platform-compatible HTML/CSS/React.js components to ensure accessibility and responsiveness for diverse healthcare users (doctors, nurses, patients) across devices.

•Built and deployed applications using Python, setuptools, Docker, and Kubernetes for scalable healthcare platforms, ensuring high availability, secure data transmission, and compliance with industry standards such as HIPAA.

•Developed and maintained microservices architecture using Flask and Django for different healthcare functionalities patient management, health analytics, sensor data processing, ensuring scalability and modularity.

•Created and optimized SQL queries and ORM models with SQLAlchemy to interact with PostgreSQL and MongoDB, ensuring secure and efficient handling of patient records and sensor data, while maintaining compliance with privacy regulations.

•Utilized AWS Lambda, API Gateway, and other AWS services (S3, RDS) to deploy serverless healthcare applications, optimizing resource usage and reducing operational costs for real-time data collection and processing.

•Developed APIs and data pipelines for seamless integration between hospital systems (EHR/EMR) and external medical devices, enabling the efficient exchange of health data for improved patient care.

•Implemented automated data storage, retrieval, and processing tasks using AWS services, ensuring compliance with healthcare data standards such as HL7 and FHIR.

•Developed machine learning-driven predictive models that analyze sensor data for early detection of anomalies (irregular heart rhythms, blood glucose levels), alerting healthcare professionals in real time.

•Coordinated the implementation of Continuous Integration/Deployment (CI/CD) pipelines for rapid deployment of healthcare features, reducing system downtime and ensuring that new updates adhere to regulatory standards.

•Optimized the performance of healthcare applications by implementing data caching, optimizing database queries, and enhancing the scalability of sensor data processing pipelines.

•Collaborated with healthcare providers to continuously gather feedback on usability and functionality, ensuring that the developed solutions meet the needs of medical professionals and patients.

•Ensured the security and privacy of patient data by implementing encryption protocols (TLS/SSL) and adhering to healthcare data security standards, including GDPR and HIPAA compliance.

Education

JNTUH University

Bachelor's, computer science May 2017

Saint peters University

Master’s in Data Science May 2023

Projects

City Water Usage Database

•Developed a database using MS Access and SQL for the NYPD to simplify the retrieval process of crime information.

•Developed a database using MS Access and SQL to simplify the retrieval process of water usage data for the city.

•Identified and resolved data consistency issues by setting up referential integrity constraints.

•Created 8 tables and 10 queries with calculations, criteria, and prompts to facilitate data analysis and reporting.

Renewable Energy Output Analysis

•Analyzed stock performance data from web APIs on Toyota Motors from 2017-2022 using Python, Pandas, and Matplotlib.

•Analyzed renewable energy production data from web APIs on various energy sources from 2018-2023 using Python, Pandas, and Matplotlib.

•Generated production trends, capacity, and 100-day moving average graphs and charts for renewable energy sources with the analyzed data.

Certifications

Microsoft Machine Learning python & Data science: https://coursera.org/verify/2ZHU3M38XCYL

Aws solution Architect: https://aws.amazon.com/verification user:ddf02ac4c6cb4689ad3dd9d9acda88a6

Django: http://www.smcertificationservices.com/national-client.php?search_key=QMS23163&submit= Credential ID 013****-****

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