Python Developer Business Operations

ELVIS MBACHECK

Sr. Python Developer

Mobile: 443-***-**** Email: **************@*****.***

SUMMARY

Senior Python Developer with over 12+ years of experience in financial services, healthcare, IT, and banking industries, specializing in building scalable, high-performance applications by utilizing Python-based frameworks and technologies to streamline processes, solve complex technical challenges, and optimize system efficiency, ensuring seamless integration with critical business operations.

Proficient in Python frameworks like Django, Flask, and libraries such as Pandas, NumPy, and scikit-learn, leveraging these tools to develop secure, maintainable applications and designing end-to-end solutions for data processing, automation, and real-time analytics, significantly enhancing decision-making and operational efficiency across various sectors.

Skilled in designing and deploying robust ETL pipelines using Apache Airflow and Pandas, automating data extraction, transformation, and loading to ensure consistent and accurate data flow across multiple systems while being well-versed in cloud technologies, particularly AWS services (EC2, S3, Lambda, and Elastic Beanstalk) for scalable, efficient infrastructure management.

Advanced knowledge of database management, optimizing performance with relational (PostgreSQL, MySQL) and NoSQL databases, and expertise in schema design, query optimization, and troubleshooting to ensure high availability and fast data retrieval, along with proficiency in fine-tuning databases to handle large-scale data for enterprise-level applications.

Expertise in API development, using Python’s Requests library and Flask to integrate third-party systems, ensuring seamless interoperability while maintaining security standards, and experienced in building secure, reliable APIs to enable smooth data exchanges between services, enhancing application functionality and reducing manual intervention in key business operations.

Experienced in machine learning and predictive analytics with frameworks such as TensorFlow and scikit-learn, using these tools to create data-driven solutions for various business applications while implementing AI models to improve decision-making processes and deliver actionable insights that enhance business outcomes and operational performance.

TECHNICAL SKILLS

Languages

Python 3.x, 3.6,3.3,2.7/2.4, C++, Java, Shell Script, Perl, SQL

Python Framework

Django 1.3/1.4/1.5, Pyramid, Flask, web2Py.

Databases

MySQL 5.1, SQL Server 2008, Oracle 10g, Siebel, PLSQL, Oracle, Microsoft SQL, MySQL, MongoDB

Web Technologies

AJAX, JavaScript, HTML, DHTML, XHTML, XML, jQuery, CSS, API

Versioning Tools

Subversion, GIT, Perforce, CVS

IDE

Eclipse, My Eclipse, PyCharm, RAD, Net beans, MS Visio, Sublime Text, Notepad++

Web servers

Apache, IIS

Frameworks

SpringBoot, FastAPI

Debugging tools

Selenium, IDE

No SQL

MongoDB, Cassandra

Machine Learning

Artificial Neural Network, Convolution Neural Network Bayesian Network/BBN, Linear Regression, Logistic Regression, Decisions Tree, Elastic-net regularized generalized linear models (built in R), k-NN, SVM, SVDK Clustering, Page Rank, PCA, MCA, Data Mining & Deep Learning Algorithms, Market Base Analysis, Bagging, Boosting.

Cloud Technologies

AWS, Azure, GCP, Kubernetes

Tools

Visual Studio, IntelliJ, PyCharm, Android Studio, Putty, FileZilla, TFS, JIRA, Rally, Version1, HP ALM, Test Track Pro, Rational team Concert, Jenkins

Operating systems

Linux/Unix, Windows NT/2000/XP/2003/Vista, Mac OSX

Version Control

Github, Sub Version

Build Tools

GNU, Apache Ant, Apache Maven, Buck, Bit-Bake, Boot, Grunt

Methodologies

Agile, Scrum

DOMAIN SKILLS

Python Programming

Object-oriented Programming

Container Orchestration

Microservices Architecture

Project Coordination

Process Enhancement

Continuous Integration

Agile Methodologies

Project Coordination

EXPERIENCE

January 2024 – Present Biogen, Cambridge, MA Senior Python Developer

Spearheading the design, development, and deployment of Python and Spark scripts tailored to healthcare solution requirements, ensuring alignment with customer specifications and industry best practices while focusing on processing healthcare data efficiently and maintaining strict compliance with regulatory standards and industry protocols in the healthcare domain.

Developed scalable applications using Python and associated frameworks such as Django and Flask, ensuring high availability and low latency in a cloud-native environment.

Collaborating closely with the customer project manager to define project goals, scope, and deliverables, ensuring Python-based solutions meet the specific needs of the healthcare domain, while ensuring timely delivery of scalable, secure, and high-performance solutions that enhance healthcare systems, patient care, and operational efficiency.

Architecting Python Spark scripts with a focus on healthcare-specific use cases and ensuring data integration, system interoperability, and compliance with healthcare regulations like HIPAA, while emphasizing efficient code and minimizing latency to enable seamless data handling across various healthcare platforms and applications.

Led the development of multiple AI-powered web applications using OpenAI’s APIs for automation tools and virtual assistants.

Designed and implemented high-throughput, low-latency, and scalable Python-based applications, solving complex algorithms for distributed systems on AWS.

Created configuration management playbooks and roles using Ansible for efficient software deployment and orchestration across cloud environments (AWS, Azure).

Utilized AWS services (EC2, ELB, RDS, S3) to build and deploy scalable, fault-tolerant web applications.

Designed and implemented real-time trading systems using Python to enable front-office traders to execute trades efficiently and monitor market data live.

Developed large-scale data processing applications using PySpark, optimizing for performance and scalability in cloud environments.

Utilized AWS services such as S3, Kinesis, Lambda, and SNS to build cloud infrastructure components and ensure optimal performance at scale.

Collaborated with DevOps teams to develop, automate, and manage CI/CD pipelines using Jenkins, Apache Airflow, and Docker, improving deployment efficiency by 40%.

Led the development of Generative AI solutions using large language models (LLMs) for a variety of applications, including text generation, fine-tuning, and domain-specific task adaptation.

Designed and developed backend systems using Python and Django, integrating with advanced AI frameworks such as Langchain and LlamIndex for document Q&A and data retrieval applications.

Developing XML Web Services in C# and SOAP for information exchange across applications.

Providing in-depth technical support during system integration testing (SIT), troubleshooting and resolving issues identified with Python Spark scripts, ensuring seamless integration with existing healthcare systems.

Spearheaded CI/CD pipeline setup using GitHub and Jenkins, automating the deployment and testing of code across development, staging, and production environments.

Developed custom prompts to guide LLM outputs effectively through advanced Prompt Engineering, ensuring the generation of high-quality, relevant text.

Enhanced the reliability and performance of existing automation scripts, optimizing resource usage and improving response times by 30%.

Designed and implemented end-to-end ETL pipelines in GCP, leveraging tools such as BigQuery, Dataflow, and Cloud Storage for efficient data processing and storage.

Implemented security best practices for automation processes, ensuring secure handling of secrets, access control, and OAuth2 authentication in automation workflows.

Implementing the DevOps concept and educating teams about its methods.

Wrote and maintained infrastructure as code (IaC) using Terraform and CloudFormation to automate cloud resource provisioning.

Designed and developed C#-based components, integrating them seamlessly with Python applications to extend functionality and improve performance.

Participated in code reviews, identified areas for improvement, and ensured clean, reusable, and maintainable code following Python best practices.

Conversion of multiple ambulatory clinics under one EHR instance.

Assisting with User Acceptance Testing (UAT) by addressing feedback, fixing identified defects, and ensuring that Python Spark scripts meet the final healthcare solution's functional and performance standards.

Coordinating with the project manager during code deployment activities to ensure the secure, efficient release of Python Spark scripts while minimizing downtime and ensuring the scripts adhere to healthcare data privacy regulations and supporting system stability throughout deployment and post-deployment monitoring in a live healthcare environment.

Optimizing Python Spark scripts to process large healthcare datasets efficiently, ensuring applications meet performance standards, handle large volumes of data, and reduce processing times, while improving data analysis, supporting faster decision-making, and enhancing overall system performance, which is critical in healthcare operations and patient care.

Delivered technical mentorship to junior developers and peers, providing guidance on best practices in cloud infrastructure, Python development, and DevOps.

Worked with system architects to design scalable automation solutions for complex infrastructure environments.

Managed and optimized Big Data workflows on GCP, ensuring cost-effective and high-performing data solutions.

Expertise in automating, developing, delivering, and releasing code from one environment to another at DevOps Engineering.

Leveraged Retrieval-Augmented Generation (RAG) to integrate external data retrieval systems with LLMs, significantly enhancing response accuracy and contextual relevance.

Integrated trading systems with order management platforms, ensuring real-time synchronization between market data and trade execution.

Worked with Excel spreadsheets and databases to extract, manipulate, and transform financial data for various products, including IRD and derivatives.

Troubleshot and debugged Python-based applications, identifying and resolving issues related to system performance and script failures.

Implemented testing suites using Pytest, ensuring code reliability and performance.

Developed Python-based scripts for automating cloud infrastructure management on AWS and Azure, including the automation of instance provisioning, configuration, and monitoring tasks.

Created Python-based tools to monitor real-time positions and track risk exposure, enabling front-office teams to manage trading risk effectively.

Implemented RESTful APIs to facilitate communication between frontend and backend, enabling the seamless integration of AI features into the platform.

Managed and optimized MySQL and NoSQL databases, ensuring high-performance query execution and effective data storage.

Mentoring junior developers and supporting them in Python and Spark development practices specific to the healthcare domain, providing guidance on best practices, encouraging knowledge sharing, improving coding standards, and facilitating their professional growth while ensuring the healthcare software development team operates efficiently.

Identifying and implementing process improvements throughout the software development lifecycle by optimizing Python Spark script deployment, testing, and troubleshooting procedures, contributing to enhanced performance, accuracy, and compliance, and delivering healthcare solutions more efficiently, ensuring they align with regulatory requirements and meet industry standards.

May 2022 – December 2023 AT&T, Dallas, TX Senior Python Developer

Spearheaded the design and development of scalable and high-performance Python-based solutions, ensuring seamless integration with telecom infrastructure and adherence to industry best practices, while optimizing application performance and reducing operational overhead across telecom systems and services.

Drove the architecture and implementation of secure, reliable, and efficient APIs and microservices using Django, Flask, or similar frameworks, ensuring their compatibility with telecom-specific platforms, technologies, and protocols for effective communication and data exchange across the network.

Led the mentoring and upskilling of junior developers, cultivating a culture of collaboration and knowledge sharing within the team, while guiding them through the complexities of telecom application development, troubleshooting, and optimization processes.

Proactively sought opportunities for code reuse and generalization of services to reduce complexity and increase maintainability.

Developed machine learning models using TensorFlow, PyTorch, and Hugging Face Transformers, integrating them into scalable AI pipelines.

Implemented backtesting frameworks in Python to test the performance of trading strategies against historical market data.

Worked closely with DevOps teams to integrate CI/CD pipelines and streamline deployment workflows.

Integrated front-office trading platforms with backend systems using Python scripts for seamless data flow and efficient trade management.

Played a key role in setting up CI/CD pipelines using Azure DevOps and Jenkins, automating testing, deployment, and versioning processes.

Contributed to backend systems and services written in C#, ensuring compatibility with Python-based tools and frameworks.

Designed and developed reliable, high-throughput systems using Python and AWS services, contributing to the performance of large-scale distributed applications.

Created dashboards in Azure DevOps for CI/CD pipelines, Work items and bugs.

Led the testing and debugging of complex Python applications in a telecom environment, ensuring the delivery of robust, error-free software that met functional requirements, performance standards, and quality benchmarks within the telecom sector.

Developed serverless applications using AWS Lambda, integrating with SNS and Kinesis for real-time data processing.

Leveraged AWS to host and scale applications, utilizing EC2, ELB, and RDS for infrastructure management.

Collaborated closely with cross-functional teams, including network engineers and product managers, to define and implement software solutions that met the evolving needs of telecom clients, ensuring timely delivery and alignment with business objectives.

Built and maintained RESTful APIs for application interaction with cloud services, ensuring data security and compliance with OAuth2 token-based authentication.

Formulated and enforced coding standards, best practices, and security protocols within the development team, ensuring that all Python-based applications complied with telecom industry regulations and security frameworks to mitigate potential vulnerabilities.

Worked on multiple full-stack projects, building backend services in Python (Flask/Django) and implementing frontend components using JavaScript, React, and Vue.js.

Participated in the full software development lifecycle (SDLC), including requirements gathering, system design, development, testing, deployment, and maintenance.

Architected and implemented cloud-based solutions for telecom clients, leveraging AWS to deploy, manage, and scale Python applications that enhanced operational efficiency and reduced costs in complex telecom ecosystems.

Implemented CI/CD pipelines using GitHub and Jenkins, automating the build, test, and deployment processes, ensuring faster release cycles.

Led the integration of containerization technologies such as Docker and Kubernetes into telecom applications, ensuring efficient deployment, scalability, and management of microservices across cloud and on-premise environments, while maintaining high availability.

Collaborated with cross-functional teams to integrate Generative AI models into end-to-end product solutions, supporting seamless interactions and improving user experiences.

Utilized Terraform for creating and managing AWS resources, including S3 buckets, IAM roles, and serverless functions.

Developed and maintained RESTful APIs for integrating web services with frontend applications.

Worked on HL7 inbound and outbound interfaces. Define data specifications and map out data crosswalks.

Established and maintained effective communication channels between technical and non-technical teams, ensuring alignment on project goals, expectations, and timelines, while providing actionable insights on technical risks, challenges, and mitigation strategies specific to telecom applications.

Drove the continuous improvement of software development processes and methodologies, including the adoption of Agile practices, to ensure efficient delivery of Python-based solutions in telecom projects, enhancing product quality, team collaboration, and stakeholder satisfaction.

July 2020 – May 2022 Lincoln Financial, Radnor, PA Lead Python Developer

Engineered advanced Python applications using Flask and SQLAlchemy to streamline retirement service operations in a financial services organization, utilizing PyCharm for debugging and performance optimization, leading to improved data management efficiency, enhanced system security, and ensured compliance with regulations.

Developed custom AI tools and automations for internal teams, leveraging libraries such as OpenAI GPT for building chatbots and automation systems.

Developed APIs to interact with generative models, enabling real-time content generation and data augmentation for business-specific use cases.

Collaborated with cloud teams to deploy and manage AI/ML workloads on AWS and Azure, optimizing model training and inference operations for performance and cost efficiency.

Led a cross-functional team of developers (both onshore and offshore) to design and implement Python-based solutions in the P&C Insurance domain.

Integrated third-party financial APIs using Python’s Requests library, Docker, and Jenkins to improve secure data exchange and interoperability between financial services and external systems, deploying scalable solutions that improved operational efficiency, reduced latency, and enhanced system functionality.

Integrated trading systems with order management platforms, ensuring real-time synchronization between market data and trade execution.

Collaborated with cross-functional teams to define software architecture, ensuring scalability, performance, and reusability.

Leveraged in-depth knowledge of P&C Insurance systems (Policy Administration, Claims Processing, Underwriting, etc.) to deliver tailored technical solutions that address specific industry needs.

Implemented advanced security protocols for financial services applications by incorporating OAuth2, SSL encryption with PyCrypto, and secure authentication practices, ensuring compliance with industry regulations, protecting sensitive retirement data, and reducing vulnerabilities, resulting in enhanced data protection.

Integrated C#-based APIs with Python code to facilitate data exchange between the frontend and backend of web applications.

Managed the deployment of generative models on AWS to scale AI-driven applications, ensuring optimized performance and cost-efficiency in production environments.

Developed and maintained critical applications for the P&C insurance domain, including policy management, claims processing, and underwriting systems, using Python and related technologies.

Enhanced application scalability by deploying Python-based solutions on AWS with services such as Elastic Beanstalk and EC2, utilizing AWS CloudWatch for real-time system monitoring, ensuring uptime for financial services applications, and building a more resilient infrastructure to support financial retirement services.

Assisted in the design and implementation of AWS infrastructure using CloudFormation and Terraform, automating the provisioning and management of cloud resources.

Integrated external data sources with AI systems, implementing Retrieval-Augmented Generation (RAG) to provide more dynamic and contextually aware responses.

Designed and implemented robust ETL pipelines with Pandas, Airflow, and PostgreSQL to automate the extraction, transformation, and loading of data for financial retirement services, reducing processing time, improving reporting capabilities, and providing better insights for academic, medical, and governmental clients.

Collaborated with cross-functional teams using GitHub for version control and code management, ensuring seamless integration and consistent code quality across projects, resolving conflicts, adhering to internal coding standards, and increasing team productivity and faster software delivery.

January 2019 – June 2020 UnitedHealthcare, Minnetonka, MN Lead Python Developer

Developed scalable healthcare applications using Python, Django, and PostgreSQL, improving operational efficiency; utilized PyCharm to ensure high availability and minimal downtime, delivering robust managed care solutions for healthcare enterprises.

Participated in the development of Python applications for data management tasks, ensuring low-latency data processing using AWS Kinesis and Lambda.

Designed automated ETL pipelines with Pandas and Airflow, reducing manual intervention and processing time ensured seamless data extraction, transformation, and loading for critical healthcare analytics in managed care operations.

Optimized database performance for healthcare systems by tuning SQL and NoSQL databases, leveraging DBeaver for management; reduced query latency, enabling faster access to critical patient and provider data for enhanced decision-making processes.

Built generative AI-driven applications that integrated with existing client systems, including dynamic document generation, content creation, and customer support automation.

Knowledge of evolving FHIR standards and interoperability using APIs.

Integrated third-party healthcare APIs using Python's Requests library and Flask to expand application functionality, enhanced patient experience and operational capabilities through streamlined interoperability with healthcare services and managed care platforms.

Contributed to the implementation of data validation processes for large-scale data pipelines running on AWS.

Implemented advanced security protocols like OAuth2, encryption with PyCrypto, and secure authentication practices; reduced vulnerabilities, ensuring compliance with HIPAA and industry standards in protecting sensitive patient and enterprise data.

Designed real-time trading performance reports and metrics, providing traders with actionable insights to make data-driven decisions.

Developed task automation tools in C# for business-critical processes, complementing Python automation efforts.

Analysis of EHR model content and user modifications.

Actively contributed to Agile workflows, including sprint planning and retrospectives, using Jira and Confluence; achieved an increase in project delivery speed and boosted in team productivity for healthcare enterprise projects.

Developed reusable Python libraries and frameworks in PyCharm to standardize development processes; promoted code reusability across healthcare projects, enhancing consistency and accelerating delivery timelines for managed care solutions.

Built and maintained automated test suites with PyTest and Selenium, ensuring high-quality code; achieved a significant reduction in production bugs, improving the reliability of healthcare applications and managed care systems.

Deployed Python applications to AWS using Elastic Beanstalk and monitored with CloudWatch; ensured scalability and reliability of healthcare solutions, supporting critical operations in managed care and healthcare enterprise services.

Utilized machine learning frameworks like TensorFlow and scikit-learn for predictive healthcare analytics; analyzed patient data in Jupyter Notebooks, enabling proactive decision-making for improved care delivery and operational efficiency in managed healthcare systems.

November 2017 – January 2019 HSBC, New York City Python Developer

Developed and maintained high-performance Python-based applications to optimize core banking services such as personal banking, loans, and investment management, ensuring secure, reliable operations, enhanced user experience, and streamlined workflows through adherence to best practices in software architecture.

Utilized PySpark for large-scale financial data processing and analysis, optimizing data flow for transactions, customer records, and loan applications, significantly reducing processing times and improving data handling to enable agile and responsive banking services.

Worked closely with Python and C# developers to maintain code consistency, ensuring smooth interaction between Python and C# components.

Supported the integration of third-party insurance systems and APIs for claims handling and underwriting workflows.

Develop wrapper shell scripts, stored procedures, RCM plug-in, jar files using RCM web services, etc.

Integrated Databricks into data engineering workflows to enhance real-time analytics capabilities and reporting, enabling strategic financial planning, streamlined compliance reporting, and rapid response to dynamic market conditions, ensuring a competitive advantage in banking services.

Optimized Python code for trading applications to improve execution speed and minimize latency, ensuring that trades were executed in near real-time.

Collaborated with internal stakeholders to analyze business requirements and translate them into effective software solutions in the P&C insurance space.

Deployed secure cloud-based banking solutions using AWS services such as S3 for secure data storage, EC2 for scalable computing, and Lambda for serverless functions, ensuring regulatory compliance, enhanced system performance, and high availability for critical financial applications.

Designed and implemented automated testing frameworks for banking applications, ensuring compliance with industry standards for security and performance, reducing the risk of outages, and delivering uninterrupted and reliable services to banking clients.

Designed and developed an end-to-end policy administration system for P&C insurance, including policy issuance, renewals, and cancellations, leveraging Python for back-end development.

April 2016 – October 2017 Verizon, New York City Python Developer

Expertly utilized Python libraries such as NumPy, Pandas, and SciPy within PyCharm to process large telecom datasets efficiently, ensuring high-performance analytics and delivering optimized data solutions for complex telecom system requirements.

Designed scalable telecom applications using Django and PostgreSQL, leveraging Visual Studio Code to maintain modular code structures; ensured seamless integration with telecom systems for enhanced performance in system management and operational projects.

Integrated RESTful APIs using Python’s Requests library, deployed telecom applications with Docker and Jenkins, and automated deployment pipelines for efficient delivery of scalable telecom systems and services to enterprise clients.

Created abstract classes and decorators with Python to improve efficiency and maintainability of telecom code, utilizing PyCharm for debugging and testing to deliver clean, reusable code in telecom network management and optimization projects.

Implemented machine learning models using TensorFlow and scikit-learn, monitored with MLflow, and presented data-driven insights through Jupyter Notebook, delivering AI-enhanced solutions to telecom clients for predictive network performance and customer insights.

Optimized database operations with SQLAlchemy and MySQL, improving query performance and schema design; used DBeaver to manage and visualize telecom databases efficiently for telecom infrastructure in network management engagements.

Deployed telecom applications on AWS using Elastic Beanstalk and monitored performance with CloudWatch, ensuring robust, scalable cloud-based solutions tailored to telecom client needs and network requirements.

September 2014 – April 2016 The Home Depot, Atlanta, GA Junior Python Developer

Contributed to developing secure APIs using Flask and SQLAlchemy for retail platforms, ensuring data accuracy and optimized MySQL performance; leveraged PyCharm to streamline development and debugging processes for seamless retail operations.

Supported the creation of scalable backend systems for retail analytics tools using Django; improved PostgreSQL database performance, and collaborated through Visual Studio Code for efficient teamwork and code integration within the retail sector.

Assisted in API integ rations for third-party retail solutions with Python's Requests library; streamlined application deployment using Docker and Jenkins for faster, reliable retail system rollouts.

Contributed to crafting user-friendly interfaces in Flask, incorporating Bootstrap, AJAX, and jQuery for responsive retail solutions; managed version control and team collaboration through Git and Bitbucket to ensure smooth development.

Gained experience in building data pipelines for retail analytics using Pandas and NumPy; utilized Jupyter Notebooks to analyze sales trends and assist in data-driven decision-making for the retail recruitment process.

May 2013 – September 2014 American Express, New York City Junior Python Developer

Explored deployment strategies on AWS, including Elastic Beanstalk for scalable systems and S3 for secure storage; monitored application performance with CloudWatch.

Participated in implementing CI/CD workflows using GitHub Actions, enabling

Contact this candidate