Python Developer

Name: Mariya Priya

Email: ************@*****.***

Phone No: 931-***-****

PROFESSIONAL SUMMARY

6+ years of experience in backend Python development using Django, Flask, and FastAPI across cloud-native, data-driven, and AI-integrated applications.

Built and deployed GenAI applications leveraging OpenAI GPT-3.5 and LangChain, orchestrating multi-step workflows through Retrieval-Augmented Generation (RAG) pipelines that improved document query precision and response context relevance by 40%

Built and deployed RAG systems integrating large language models with vector databases using LangChain and FAISS to enhance contextual search accuracy.

Developed and scaled RESTful and GraphQL APIs using Flask, FastAPI, and Django, backed by PostgreSQL and MongoDB, to support high-concurrency microservices with sub-100ms response times and seamless data access across distributed systems.

Automated end-to-end infrastructure provisioning and deployment pipelines using Terraform, GitHub Actions, Jenkins, and Ansible, enabling repeatable, zero-touch deployments across staging and production, which cut release cycles by 30%.

Designed and executed ETL workflows using AWS Glue, Apache Airflow, and PySpark to process high-volume structured and unstructured data.

Integrated end-to-end machine learning pipelines using AWS SageMaker, MLflow, and Kubernetes, supporting automated model training and deployment.

Architected serverless and event-driven applications with AWS Lambda, API Gateway, and Step Functions, improving system scalability and cost-efficiency.

Optimized query performance and database response time through indexing, partitioning, and caching mechanisms using Redis, Memcached, and PostgreSQL.

Implemented secure API authentication and authorization using OAuth2, JWT tokens, and Role-Based Access Control (RBAC) in enterprise-grade applications.

Integrated Azure AI Search with OpenAI-based applications to build intelligent recommendation systems powered by hybrid semantic search and embedding-based ranking models.

Engineered distributed systems using RabbitMQ and Celery to offload intensive workloads like model retraining and data validation, enabling 3x throughput and zero downtime in peak traffic scenarios.

Designed and automated a document processing pipeline using AWS Textract, Python, and Lambda to extract and normalize structured data from insurance claim scans, accelerating data ingestion for analytics by 60%.

Collaborated cross-functionally with data scientists to integrate machine learning models into production-ready Python APIs with real-time inferencing.

Experienced in mentoring junior ML engineers, conducting peer reviews, and guiding model development best practices.

Skilled in distributed training with Spark and Ray for large-scale ML pipelines.

Proficient in test-driven development using Pytest, Selenium, and JMeter to ensure performance, reliability, and code quality across deployments.

Deep understanding of cloud-based architecture (AWS, GCP, Azure), big data tools (Spark, Kafka), and agile software development practices.

Experienced in integrating server-side logic with client-side interfaces using HTML, CSS, and JavaScript, ensuring seamless user experiences.

Refactored monolithic Python systems into loosely coupled, Dockerized microservices, improving deployment agility and enabling parallel feature development across multiple teams, resulting in 50% faster release velocity.

Deployed containerized applications using Docker and orchestrated them via AWS ECS and Kubernetes, enabling seamless CI/CD and auto-scaling.

Developed and deployed high-performance RESTful services using FastAPI to serve LLM-based inference requests, implementing custom batching, throttling, and retry strategies that reduced average response time by 40% in production.

Designed robust data ingestion pipelines using Python, Pandas, and Apache Airflow to extract, transform, and load data from legacy systems and external APIs, populating enterprise data lakes and improving reporting accuracy by 25%.

Built developer-facing documentation portals and integrated Swagger/OpenAPI specifications for REST APIs to enable internal team onboarding and external consumption.

Worked with feature stores (like Feast) and model registries to manage ML metadata across training and production workflows.

Implemented Git branching strategies (GitFlow) and pull request workflows across teams using GitHub/Bitbucket and enforced code quality via pre-commit hooks and linters.

Engineered custom automation scripts using Boto3 to manage AWS EC2, S3, IAM, and CloudWatch, including dynamic environment setup and failover handling, improving infrastructure resilience and deployment speed.

Built real-time observability pipelines using Grafana, Prometheus, and AWS CloudWatch to monitor API health, set up proactive alerts, and reduce production issue detection time by over 60%.

Developed concurrent backend services using Golang, implementing lightweight threads for efficient task orchestration in high-throughput systems.

Designed and automated network test cases using Python and pyATS to validate routing protocols (BGP, OSPF, ISIS)

Streamlined cloud-native MLOps workflows by scripting automated infrastructure and deployment tasks using Bash, Python, and Shell scripts, reducing manual errors and setup time across multiple environments.

Built classification and regression models using TensorFlow, PyTorch, and Scikit-learn, supporting real-time and batch inferencing across multiple production pipelines.

Led full ML data lifecycle — from ingestion, feature engineering, and model development to deployment, monitoring, and continuous retraining.

Strong foundation in Object-Oriented Programming using Python and Java for scalable, reusable software architecture.

Skilled in authoring architecture docs, test plans, and design specs across agile SDLC stages to support high-quality software delivery.

Currently expanding backend expertise with Golang, focusing on concurrency, microservice patterns, and system performance optimization.

Familiar with Cypress for UI test automation and Agile development using Jira.

TECHNICAL SKILLS:

Languages

Python (2.x, 3.x), Java, Golang, SQL, Shell Script, Bash, Perl, C++, Golang

Web Frameworks & Libraries:

Django, Flask, FastAPI, LangChain, Transformers, PyTorch, TensorFlow,

Scikit-learn, Pandas, NumPy, spaCy, NLTK, Gensim

GenAI & LLM Ecosystem:

OpenAI GPT-3.5, LangChain, FAISS, RAG Pipelines, Azure AI Search,

HuggingFace, Claude

Cloud Platforms & Tools:

AWS (EC2, S3, Lambda, Glue, API Gateway, IAM, Redshift, DynamoDB,

Textract, Step Functions, CloudWatch, ECS, CDK),

GCP (BigQuery, Pub/Sub, Cloud Functions), Azure, Boto3, Azure DevOps

DevOps & Automation:

Terraform, Ansible, GitHub Actions, Jenkins, Docker, Kubernetes,

CloudFormation, Bash Scripting, Shell Scripting, Git, GitFlow, Bitbucket

APIs & Web Technologies:

RESTful APIs, GraphQL, Swagger/OpenAPI, OAuth2, JWT, RBAC, HTML5,

CSS3, JavaScript, TypeScript, React, Redux, Vue.js, Node.js, AJAX, JSON, XML

Data Engineering & ETL Pipelines:

Apache Airflow, AWS Glue, PySpark, Apache Spark, Apache Beam, Kafka,

Kinesis, Pandas, AWS Redshift

Databases & Storage:

PostgreSQL, MySQL, Oracle, MongoDB, Cassandra, DynamoDB, Redis,

Memcached, SQL Server

Testing & Quality Assurance:

Pytest, UnitTest, Selenium, JMeter, Postman, pyATS, Genie

Monitoring & Observability:

Prometheus, Grafana, ELK Stack, AWS CloudWatch

IDEs & Dev Tools:

VS Code, PyCharm, Jupyter Notebook, Eclipse, NetBeans, Spyder, Git,

GitHub, Bitbucket, Cypress, Jira

Business Intelligence & Visualization:

Power BI, DAX, Power Query, Dataiku, Tableau, Matplotlib, Seaborn, Bokeh

PROFESSIONAL EXPERIENCE:

Client: Optum June 2024 – Till Date

Role: Python Full Stack Developer

Responsibilities:

Implemented business logic using Python/Django.

Responsible for setting up Python REST API framework using Django.

Implemented CI/CD automation using Jenkins and Terraform, reducing deployment time by 30%.

Administered Kubernetes clusters on AWS EKS, implemented IaC using Terraform, and automated CI/CD workflows via GitHub Actions for microservices deployment and platform operations

Developed ETL workflows using AWS Glue and Apache Airflow to automate data transformations.

Built GraphQL APIs for seamless data exchange between microservices.

Designed and implemented a prototype Retrieval-Augmented Generation (RAG) system integrating OpenAI GPT-3.5 with domain-specific documents using FAISS for vector indexing and LangChain for retrieval orchestration.

Integrated Django-based internal tools with Salesforce CRM using REST APIs to automate customer data sync and streamline lead management workflows.

Developed an OCR-driven data extraction pipeline using AWS Textract, automating the processing of scanned insurance claims and integrating extracted data into backend APIs for validation and downstream analytics.

Designed asynchronous and distributed systems by integrating RabbitMQ for queue-based message handling, improving system scalability and fault tolerance.

Automated AWS infrastructure tasks using Boto3 and Python scripts to manage services such as EC2, S3, Lambda, and CloudWatch, improving deployment efficiency and reducing manual errors by 40%.

Integrated real-time monitoring and alerting pipelines using Grafana, Prometheus, and AWS CloudWatch to ensure system health and performance.

Developed and maintained scalable GraphQL and RESTful APIs using Flask and AWS API Gateway, integrating with backend services and enabling real-time data access across client-server systems.

Designed and deployed scalable data pipelines and APIs on Google Cloud Platform using Cloud Functions, Pub/Sub, and BigQuery. Integrated GCP services with AWS-based workflows for hybrid cloud flexibility.

Designed and maintained databases using Python and developed Python-based API (RESTful Web Service) using Flask, SQLAlchemy, and PostgreSQL.

Implemented performance tuning and improved the performance of stored procedures and queries.

Worked on functions in AWS Lambda that aggregate data from incoming events and then store result data in Amazon DynamoDB.

Collaborated on backend module design in Go and Rust during MVP prototyping stages to explore high-performance backend options.

Configured AWS Identity and Access Management (IAM) Groups and Users for improved login authentication.

Configured and automated various AWS services like EC2 and S3 using Boto (Python SDK).

Designed and Developed ETL Processes in AWS Glue to migrate campaign data from external sources like S3, ORC/Parquet/Text Files into AWS Redshift.

Wrote Ansible Playbooks with Python, SSH as the wrapper to manage configurations of AWS nodes and tested Playbooks on AWS instances using Python. Ran Ansible scripts to provision Dev servers.

Wrote Terraform templates for AWS Infrastructure as a Code to build staging and production environments & set up build & automation for Jenkins.

Mentored junior data science and engineering staff on ML best practices, peer-reviewed model codebases, and facilitated hands-on training sessions on production ML workflows.

Utilized Spark and Ray for distributed model training across large-scale transactional datasets, improving training time by 40%

Built TorchServe-based model inference services integrated with FastAPI APIs for scalable production deployment.

Developed automated data pipelines using Python and integrated them with Power BI to generate interactive dashboards and real-time business intelligence reports for operational insights.

Built and optimized data models in Power BI using DAX and Power Query, enhancing data visualization and performance for KPIs, financial reports, and executive dashboards.

Optimized large data transformations using Python’s multiprocessing library to accelerate ETL and reporting workflows.

Worked with JIRA for Agile project management, tracking issues, and managing software development tasks.

Experienced working on healthcare data platforms and claims-processing APIs aligned with HIPAA-compliant standards

Environment: Python 3.9/3.7, Django, Flask, FastAPI, PostgreSQL, MySQL, MongoDB, DynamoDB, AWS (EC2, Lambda, Glue, S3, Textract, CloudWatch), GCP (BigQuery, Pub/Sub, Cloud Functions), Terraform, Jenkins, GitHub Actions, Ansible, Apache Airflow, PySpark, Power BI, JIRA, Shell Scripting, HTML5, CSS3, JavaScript, Node.js, React, REST, GraphQL, SQLAlchemy, PyCharm, Jupyter Notebook

Client: Ford Sep 2023 – May 2024

Role: Python Developer

Responsibilities:

Developed scalable REST APIs using FastAPI and Flask, including endpoints for LLM-based inference integrated with AWS Lambda for on-demand response generation.

Built and maintained backend services using Django and Golang, deployed via Lambda and Fargate to ensure high scalability and low latency.

Designed and optimized PostgreSQL databases, reducing query latency by 40% through performance tuning, indexing, and query refactoring.

Automated infrastructure using Terraform, AWS CloudFormation, and Jenkins, supporting full CI/CD pipelines with GitHub, Docker, and Gradle.

Designed and monitored streaming data pipelines using Kafka, PySpark, and AWS Kinesis for real-time data ingestion and processing.

Integrated MLOps workflows with SageMaker and MLflow for model deployment and lifecycle management.

Developed interactive UI components using Angular, Bootstrap, HTML, and CSS, connected to backend services via RESTful APIs.

Created reusable Python scripts to automate network configuration tasks, data parsing, and information retrieval from connected devices.

Created ETL processes with AWS Lambda to transition data from DynamoDB to Redshift, enabling analytics on transactional datasets.

Used Grafana and CloudWatch to set up real-time monitoring dashboards and alerts for AWS infrastructure performance.

Performed code reviews and contributed to establishing Python development best practices in a collaborative Agile team environment

Engineered high-performance backend systems for near real-time data processing (sub-100ms) with Python and Golang; integrated monitoring and fault-detection using AWS CloudWatch, Prometheus, and event-driven alerting pipelines.

Deployed containerized services using Docker and AWS ECS for fault-tolerant, scalable backend systems.

Wrote automated tests for REST APIs using Postman and PyUnit; integrated with Jenkins pipelines for continuous testing.

Developed web services with SOAP and XML formats for legacy system integrations.

Developed backend APIs for near real-time data operations, integrated with AWS CloudWatch for event-driven alerts and performance tracking.

Managed roles, permissions, and MFA access policies using IAM to enforce security best practices.

Used AWS Beanstalk and EC2 to deploy and manage web applications with auto-scaling and versioning support.

Provided production-level support for Kubernetes platforms with 24/7 on-call rotation and performed root cause analysis for infrastructure issues

Participated in Agile/Scrum sprints, sprint planning, and weekly release meetings to drive timely and stable software delivery.

Environment:

Python, Django, Flask, FastAPI, Golang, PostgreSQL, MongoDB, MySQL, AWS (EC2, S3, Lambda, DynamoDB, Redshift, CloudFormation, ECS, Kinesis, Beanstalk, IAM, CloudWatch), Terraform, MLflow, SageMaker, Jenkins, Docker, Git, PySpark, Kafka, Angular, HTML, CSS, JavaScript, Bootstrap, Postman, Grafana, Agile, PyCharm

Client: Efftronics Systems Pvt Ltd, India Jan 2021 – July 2023

Role: Python Developer

Responsibilities

Developed entire frontend and backend modules using Python on Django Web Framework.

Designed and Developed REST Webservices to interact with various business sectors and used SOAP protocol for web services communication.

Wrote scripts for ETL jobs and Glue workflows to trigger Data Migration (e.g., AWS Glue, AWS Step Functions).

Developed and deployed AWS Lambda Functions to automate data processing tasks, resulting in a 20% improvement in data processing time.

Created Restful microservices utilizing Flask and Django and sent them on AWS servers utilizing AWS Elastic Beanstalk (Web Apps) and Amazon EC2 instances.

Integrated AWS Lambda Functions with API Management to create RESTful APIs, enhancing system scalability and flexibility.

Implemented asynchronous tasks using Celery with Flask, enabling background processing for tasks such as sending emails or processing large datasets.

Utilized Amazon DynamoDB for cross-region replication, ensuring high availability and disaster recovery capabilities.

Developed data analytic tools using Python Pandas and visualizations using Matplotlib and Bokeh.

Utilized PyUnit, the Python Unit test framework, for all Python applications.

Crafted responsive and user-friendly web interfaces using JavaScript Framework.

Worked with AWS Lambda Functions to create functions to connect and perform various operations against different databases.

Implemented unit tests and integration tests for Flask applications using frameworks like Pytest, ensuring code reliability and stability.

Leveraged Flask extensions like Flask-Cache to implement caching mechanisms, improving application performance.

Deployed Flask applications on cloud platforms like AWS, utilizing services such as AWS Elastic Beanstalk for scalable hosting.

Rewrote existing Python/Django modules to deliver certain formats of data.

Applied SQL querying extensively across Oracle, SQL Server, MongoDB, and MySQL databases in past roles, enabling robust data analysis and reporting functionalities.

Created AWS CloudFormation templates to connect to Glue and databases (e.g., AWS RDS, DynamoDB) and created stacks.

Utilized Oracle, SQL Server, MongoDB, and MySQL in past projects to manage and manipulate large volumes of data efficiently.

Worked on optimizing the Pyspark jobs to run on the Amazon Elastic Kubernetes Service (EKS) cluster for faster processing.

Worked on creating and running Pyspark jobs to load data from on-prem databases and DB2 to Amazon S3 Storage.

Implemented decoupled and scalable microservices architecture using Amazon SQS, reducing system dependencies and improving fault tolerance.

Implemented logging and monitoring solutions for Flask applications using tools like ELK stack or Prometheus, enabling real-time visibility into application performance and errors.

Web development includes standardizing the toolsets used from Eclipse to use Git for source control.

Performed troubleshooting, fixed, and deployed many Python bug fixes of the two main applications that were the main data source for both customers and the internal customer service team.

Collaborated in automation tasks using both Python and Java for backend services, contributing to shared codebases across data ingestion and reporting modules.

Utilized Java for scripting and OOP-based integrations in systems interacting with third-party APIs and batch processors.

Environment:

Python, Django, HTML, CSS, XML, QML, JavaScript, GO Lang, AJAX, Webserver, Matplotlib, NumPy, PyDev, PostgreSQL, Apache, Bootstrap, Oracle, PL/SQL, MySQL, MS SQL, Web Services, REST, PyCharm, Windows, Linux.

Client: Ventech Software Solutions Pvt Ltd, India May 2019 – Dec 2020

Role: Software Developer

Responsibilities:

Worked on gathering requirements, system analysis, design, development, testing, and deployment.

Developed rich user interface using CSS, HTML, JavaScript, and Angular.

Used Angular to select DOM elements when parsing HTML.

Wrote PYTHON modules to extract/load asset data from the MySQL source database.

Created a database using MySQL and wrote several queries to extract/store data from the database.

Implemented database access using Django ORM.

Designed RESTful XML web service for handling AJAX requests.

Used Amazon Cloud EC2 along with Amazon S3 bucket to upload and retrieve project history.

Set up automated cron jobs to upload data into the database, generate graphs and bar charts, upload these charts to the wiki, and back up the database.

Troubleshooting ETL issues in SQL Server Integration Services.

Skilled in using collections in Python for manipulating and looping through different user-defined objects.

Used Git for version control.

Actively participated in system testing, production support, and maintenance/patch deployments.

Developed Automation Scripts for Regression using Selenium Web Driver and Python.

Implemented user interface guidelines and standards throughout the development and maintenance of the website using HTML, CSS, JavaScript, and jQuery.

Used extensively JavaScript and ASP.NET Ajax for front-end and Ajax framework.

Effectively communicated with external vendors to resolve queries.

Experience in working closely with offshore development and production support teams and responsible for collecting daily status updates and communicating them.

Environment: Python, HTML, XHTML, CSS, JavaScript, Angular, Eclipse, MySQL, Windows OS.

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