- Implemented Jenkins CI/CD pipelines to automate the build, testing, and deployment of API services, ensuring efficient and consistent software delivery.- Utilized DynamoDB for efficient data storage and retrieval in API applications.- Integrated Amazon S3 (Simple Storage Service) to securely store and manage large volumes of data within the API ecosystem.- Implementing caching mechanisms using Redis to optimize API performance and reduce database load.- Leveraged OpenAPI specifications to document and expose APIs, enabling clear communication and easy integration with external systems.- Implemented authentication and authorization mechanisms, OAuth 2.0, to secure API endpoints and protect sensitive data.- Utilized containerization technologies (Docker) to create lightweight and portable environments for API deployment.- Worked with message queues (Apache Kafka) to enable asynchronous communication and decoupling of services in API architectures.- Collaborated with other developers to provide comprehensive API documentation and support for downstream consumer integration needs.- Monitoring API performance using Datadog and Splunk, enabling proactive identification and resolution of issues.- Conducted load and performance testing using JMeter to ensure API scalability and stability under high traffic conditions.- Implemented logging and error tracking mechanisms to capture and analyze API logs, enabling quick troubleshooting and issue resolution.- Utilized version control systems (Git & Bitbucket) for efficient code management and collaboration within the development team.- Followed agile methodologies (Scrum) to facilitate iterative development and timely delivery of API features and enhancements.- Actively participated in peer code reviews and utilized static code analysis tools SonarQube and SonarLint to maintain code quality and adhere to best practices.

- Created and maintained data pipelines to ingest structured and unstructured data from various sources into the data lake, ensuring data quality and integrity.- Worked with the data cataloging tool Apache Atlas to organize and categorize data assets within the data lake, improving discoverability and usability.- Developed and optimized data ingestion processes to efficiently extract, transform, and load (ETL) large volumes of data into data processing frameworks Hadoop and Apache Spark.- Implemented data integration solutions (Apache Kafka) for real-time or near-real-time data ingestion and streaming.- Designed and implemented data connectors and adapters to integrate with various data sources, including relational databases, APIs, and third-party services.- Leveraged distributed processing frameworks Apache Spark and Flink for scalable and parallel data processing, enabling faster analytics and insights.- Built data pipelines to transform and cleanse raw data into structured and usable formats for downstream analytics and reporting purposes.- Implemented batch processing workflows using frameworks Apache Airflow and BMC Control-M for scheduling and orchestrating data processing jobs with dependencies.- Designed and implemented streaming data processing pipelines using Apache Flink, enabling real-time data analytics and event-driven architectures.- Utilized Apache Hive and Apache Impala for querying and analyzing large datasets stored in Hadoop or cloud-based data platforms.- Implemented data visualization and reporting solutions using Apache Superset to present insights and analytics derived from big data.- Worked with columnar data formats (Parquet) for optimized storage and retrieval of big data, improving query performance and resource utilization.- Utilized distributed storage and processing frameworks (Apache Cassandra) for high-performance and scalable data storage and retrieval.

- Cloud Content Integration Team at Sonos, specializing in integrating cloud services and content for seamless music playback. ("Hey Sonos... play today’s top hits!")- Ensured software quality by designing and executing comprehensive test plans, test cases, and test scripts for various features and functionalities of the Cloud Content Integration platform.- Collaborated closely with developers and product owners to understand requirements, identify testable scenarios, and provide early feedback on potential quality issues.- Developed and maintained automated test suites using JUnit to increase testing efficiency and coverage.- Implemented continuous integration and continuous deployment (CI/CD) pipelines using Jenkins, enabling automated builds, testing, and deployment of software applications.- Integrated automated tests into the CI/CD pipeline, ensuring that every code change undergoes rigorous automated testing before being deployed to production.- Implemented automated contract testing using PACT framework to validate and ensure compatibility between different microservices and APIs involved in the Cloud Content Integration platform.- Collaborated with cross-functional teams, including developers, product managers, and architects, to define contract specifications and implement contract tests to verify interactions and data exchanges between services.- Actively participated in code reviews, providing feedback on code quality, testability, and ensuring adherence to testing best practices and quality standards.- Conducted exploratory testing to uncover edge cases, usability issues, and potential vulnerabilities in the Cloud Content Integration platform.- Collaborated with the development team to investigate and troubleshoot issues, providing detailed bug reports and contributing to the resolution process.

- Worked on developing and implementing machine learning models and algorithms to solve healthcare-related challenges and improve patient outcomes.- Collaborated with cross-functional teams, including data scientists, clinicians, and software engineers, to understand business requirements and translate them into machine learning solutions.- Assisted in data preprocessing, cleaning, and feature engineering to prepare datasets for model training and evaluation.- Implemented various machine learning algorithms: decision trees, random forests, and neural networks, to solve classification, regression, and clustering problems.- Utilized Python and popular libraries/frameworks scikit-learn, TensorFlow, and PyTorch for model development, training, and evaluation.- Conducted experiments and performed hyperparameter tuning to optimize model performance and generalization ability.- Developed and deployed predictive models for disease diagnosis, patient risk stratification, and treatment recommendation.- Conducted thorough analysis and interpretation of model outputs, providing actionable insights and recommendations to healthcare professionals.- Assisted in integrating machine learning models into existing healthcare systems and applications for real-time decision support and automation.- Implemented data pipelines and workflows to ensure efficient and scalable data processing, model training, and inference.- Collaborated with the data engineering team to design and optimize data storage and retrieval systems for large-scale healthcare datasets.- Followed best practices for model versioning, documentation, and reproducibility to ensure transparency and facilitate future enhancements.- Worked on evaluating the ethical implications and fairness of machine learning models, addressing issues related to bias, privacy, and security in healthcare data.

- Managed the financial operations as the CFO of a College Investment Fund specializing in commodity trading through Exchange-Traded Funds (ETFs).- Oversaw the fund's financial planning, budgeting, and forecasting processes to optimize investment strategies and maximize returns.- Conducted in-depth analysis of commodity markets, including energy, metals, agriculture to identify profitable trading opportunities.- Developed and implemented risk management strategies to mitigate exposure to price volatility and market fluctuations in commodity trading.- Utilized financial modeling techniques and tools to assess investment performance, evaluate portfolio risk, and make informed trading decisions.- Collaborated with investment analysts and traders to formulate trading strategies based on market research, historical data, and quantitative analysis.- Monitored and analyzed financial market trends, news, and events to anticipate potential impacts on commodity prices and adjust trading strategies accordingly.- Utilized financial software and trading platforms, Bloomberg Terminal and Thomson Reuters Eikon, to access real-time market data, track portfolio performance, and execute trades.- Conducted thorough due diligence on ETFs and their underlying commodities to ensure compliance with fund objectives, regulatory requirements, and risk management policies.- Prepared financial reports, statements, and presentations for stakeholders, including investors, board members, and advisors, providing insights into fund performance and investment strategies.- Managed relationships with external financial institutions, brokers, and custodians to optimize trade execution, settlement, and portfolio management processes.- Stayed updated with industry trends, regulatory changes, and best practices in commodity trading and investment management to drive continuous improvement and ensure regulatory compliance.