Senior Python Full-Stack Developer

MURALI KRISHNA

Email: ******************@*****.*** PH: 385-***-****

Senior Python Full Stack Developer

PROFESSIONAL SUMMARY

9+ years of professional experience as a Python Developer with expertise in backend development, RESTful APIs, microservices, cloud platforms, data engineering, and AI/ML-enabled enterprise applications.

Strong hands-on experience developing scalable backend applications using Python, Django, Django REST Framework, FastAPI, Flask, RESTful APIs, Microservices, Django ORM, and SQLAlchemy.

Experienced in building enterprise-grade backend services across financial research, healthcare analytics, retail operations, banking workflows, reporting systems, and internal business applications.

Hands-on experience with modern AI/ML and LLM technologies, including LLM, RAG, OpenAI API, LangChain, Prompt Engineering, Embeddings, FAISS, Vector Search, Semantic Search, and scikit-learn.

Architected REST-based service layers and microservices that facilitated secure communication between enterprise systems, automated business processes, and streamlined integration with downstream applications.

Automated recurring operational workflows, validation processes, reporting activities, and batch-processing tasks, reducing manual intervention and improving overall processing efficiency.

Built cloud-based processing solutions to automate file ingestion, transaction validation, reporting workflows, and secure API communication across distributed enterprise applications.

Experience in using Scikit-Learn and Stats models in Python for Machine Learning and Data Mining.

Experience in analysing data using Python, R, SQL, Microsoft Excel, Hive, PySpark, and Spark SQL for Data Analytics, Data Cleansing azure and Machine Learning.

Skilled experience in Python with using new tools and technical developments (Libraries Used: libraries - Beautiful Soup, Jasy, NumPy, SciPy, Matplotlib, Pickle, PySide, Panda’s data frame, NetworkX, urllib2, Pychart, Highcharts) to drive improvements throughout entire SDLC.

DevOps / CI-CD: Docker, Kubernetes (EKS), Jenkins, Bamboo, GitHub Actions, Bitbucket - containerised microservices architecture with automated test-gate deployment pipelines.

Security & compliance: JWT, OAuth2, HashiCorp Vault, AWS Secrets Manager, Qualys FIM - built platforms supporting GRC, HIPAA-adjacent, and financial-regulatory workflows.

Databases: PostgreSQL, MySQL, MongoDB, DynamoDB, Redis, Snowflake - ORM design (SQLAlchemy, Django ORM), stored procedures, index tuning, and query optimisation.

Strong AWS experience using EC2, S3, Lambda, RDS, API Gateway, SQS/EventBridge, CloudWatch, DynamoDB, and Amazon Redshift for backend services, serverless workflows, data processing, analytics, monitoring, and production support.

Experience with Azure services including Azure App Service, Azure Virtual Machines, Azure Blob Storage, Azure Monitor, Azure Active Directory, Azure Key Vault, Azure Repos, and Azure Pipelines for secure enterprise application deployment and monitoring.

Experience with Google Cloud services including Google App Engine, Google Compute Engine, Google Cloud Storage, Cloud IAM, Cloud Logging, and Cloud Monitoring for application hosting, storage, access control, and operational monitoring.

Hands-on DevOps experience using Docker, Jenkins, Git, GitHub, CI/CD pipelines, Linux, Gunicorn, Nginx, and Uvicorn for build automation, containerized deployments, environment promotion, and production release workflows.

Experienced in frontend integration using React.js, AngularJS, JavaScript, jQuery, AJAX, HTML5, CSS3, and JSON, enabling dashboards and web interfaces to consume backend APIs and display business, healthcare, financial, and AI-generated insights.

Strong testing experience using PyTest, PyUnit, Postman, unit testing, API validation, regression testing, and UAT support to ensure reliable backend, ETL, API, and AI workflow delivery.

Skilled in production troubleshooting and issue resolution using JIRA, AWS CloudWatch, Linux logs, Wireshark, and Fiddler across backend APIs, ETL jobs, cloud deployments, and integration workflows.

Proven ability to work in Agile/Scrum environments, collaborating with data science, QA, DevOps, frontend, business, and product teams to deliver secure, scalable, production-ready backend, cloud, data, ML, and AI-enabled solutions.

PROFESSIONAL EXPERIENCE

Goldman Sachs, Charlotte, NC

Dec 2024 – Present

Senior Python Full-Stack Developer

•Engineered end-to-end backend services for the AI Research Assistant Platform, AskResearchGPT Research Insights Module, supporting financial research retrieval, document ingestion, semantic search, LLM-based summarization, RESTful API delivery, and production deployment.

•Contributed to backend system design and microservices architecture, defining scalable service layers for document processing, retrieval orchestration, Large Language Model (LLM) workflows, API integration, and operational support.

•Designed and developed scalable Django backend services using Django REST Framework, Django models, Django ORM, and service-layer architecture to manage research data, document metadata, user queries, business rules, and insight-generation workflows.

•Developed Django ORM models, Generic Views, Class-Based Views (CBVs), and Function-Based Views (FBVs) to implement scalable backend services and business workflows.

•Implemented IAM-based access controls, environment configuration, secrets management practices, and secure API communication for cloud-hosted backend services.

•Automated cloud deployment activities using Docker, Terraform, CircleCI, GitHub, and Linux scripts to improve consistency across development, testing, and production environments.

•Monitored cloud-based applications using Splunk, New Relic, centralized logging, health checks, and performance metrics to identify failures and improve production reliability.

•Supported cloud migration and modernization initiatives by converting legacy Python workflows into containerized microservices with REST APIs, asynchronous processing, and CI/CD deployment pipelines.

•Analyzed business requirements, existing system functionality, and technical specifications by collaborating with SMEs to define scalable backend solutions.

•Designed asynchronous FastAPI services using asyncio and concurrent futures to improve throughput, reduce API latency, and support high-volume concurrent request processing.

•Performed profiling and performance tuning of Python services, optimizing memory utilization and improving response times for large-scale financial workloads.

•Developed automation scripts using Python and PowerShell to streamline operational workflows and infrastructure management tasks.

•Developed Retrieval-Augmented Generation (RAG) pipeline components covering document preprocessing, chunking, embedding generation, vector indexing, retrieval logic, prompt construction, prompt engineering, and LLM response orchestration.

•Implemented FAISS-based vector search, vector embeddings, vector indexing, embedding storage patterns, and similarity search, applying Vector Database concepts to retrieve contextually relevant financial research content for RAG workflows.

•Integrated OpenAI API with LangChain to generate context-aware financial summaries, research insights, question-answering responses, and structured outputs from retrieved content.

•Configured and maintained CI/CD pipelines using CircleCI and GitHub for automated build, testing, deployment, and release management.

•Developed enterprise backend applications using Django and Django REST Framework, exposing scalable RESTful APIs for financial services.

•Designed and developed cloud-native Python microservices using FastAPI, Django REST Framework, and Flask, delivering scalable REST APIs, asynchronous workflows, and secure integrations for enterprise banking applications.

•Implemented FastAPI services with request and response validation, API versioning, exception handling, database integration, and reusable service layers to support high-performance microservices and internal banking workflows.

•Designed and optimized PostgreSQL databases hosted on Amazon RDS for transactional processing, reconciliation reporting, schema optimization, SQL tuning, and high-volume operational data management.

•Leveraged AWS Athena and Amazon S3 to process high-volume operational datasets, reconciliation records, audit requests, historical payment data, and downstream analytics workflows using SQL-based reporting solutions.

•Established CloudWatch monitoring and alerting solutions that improved operational visibility and accelerated issue detection across critical payment-processing services.

•Developed browser automation and workflow automation solutions using Playwright and Python to streamline data collection, validation, and operational processes.

•Collaborated with infrastructure teams supporting AWS, Azure, and GCP environments to deploy and manage containerized applications on Kubernetes, leveraging IAM, Cloud Monitoring, Cloud Logging, and cloud-native services to ensure secure, reliable, and scalable application delivery.

•Collaborated with ServiceNow catalog and workflow teams to automate operational request fulfillment processes.

•Worked with Oracle and PostgreSQL databases to support financial data validation, reconciliation, and regulatory reporting workflows, developing complex SQL queries and optimizing data retrieval for analytics applications.

Environment: Python, Django, Django REST Framework, FastAPI, Flask, REST APIs, Microservices, LangChain, OpenAI API, RAG, FAISS, Prompt Engineering, PostgreSQL, Oracle, Amazon RDS, Amazon S3, AWS Athena, IAM, CloudWatch, Azure, Docker, Kubernetes, Terraform, CircleCI, GitHub, Git, Jenkins, Playwright, ServiceNow, PowerShell, asyncio, Splunk, New Relic, Linux, JSON, SQL, Agile/Scrum.

Verizon, New York, NY

May 2023 – Nov 2024

Senior Python Developer

Designed and developed end-to-end telecom analytics platform components using Python, Django, Django REST Framework, FastAPI, REST APIs, Microservices, MySQL, DynamoDB, and Amazon Redshift to support network analytics, customer usage monitoring, service assurance, and operational reporting.

Built Django ORM models, business logic layers, and REST API endpoints to manage subscriber profiles, network usage data, service requests, device inventories, billing records, provisioning workflows, outage tracking, and customer support operations.

Designed and developed scalable enterprise backend applications using Python, Django, Django REST Framework, FastAPI, REST APIs, and Microservices to support high-volume telecom analytics and operational workflows.

Developed FastAPI-based backend services using Pydantic and Uvicorn for low-latency API processing, request validation, enterprise integration, and service-to-service communication.

Engineered Python data pipelines using Pandas, NumPy, JSON, XML, SQL, AWS services, and enterprise data sources to ingest, validate, normalize, transform, and integrate high-volume datasets.

Developed distributed and event-driven processing solutions using Python, Kafka, Celery, Redis, AWS Lambda, SQS, and EventBridge to support scalable and reliable enterprise workflows.

Implemented asynchronous background processing using Celery and Redis for ETL processing, batch ingestion, scheduled reporting, data aggregation, and long-running backend workflows.

Engineered Python ETL pipelines using Pandas, NumPy, JSON, XML, lxml, and SQL to ingest, validate, normalize, and transform subscriber usage records, network performance metrics, call detail records (CDRs), billing data, and operational datasets for enterprise analytics.

Developed predictive analytics workflows using Scikit-learn to analyze subscriber usage behavior, network congestion patterns, service quality metrics, customer churn indicators, and operational performance reporting.

Evaluated analytical models using Precision, Recall, F1-Score, ROC-AUC, Confusion Matrix, and performance metrics to improve telecom service optimization and customer experience analytics.

Generated subscriber usage insights, service quality metrics, operational KPIs, and predictive indicators, integrating analytics results into backend services supporting enterprise dashboards and reporting systems.

Implemented Celery background jobs with Redis for asynchronous ETL processing, batch data ingestion, scheduled report generation, network usage aggregation, and large-scale backend processing.

Utilized Redis caching to improve response times for frequently accessed subscriber profiles, network statistics, service availability data, and operational dashboard metrics.

Automated AWS cloud workflows using Lambda, Boto3, S3, EC2, RDS, DynamoDB, Amazon Redshift, IAM, VPC, and CloudWatch to support secure telecom data ingestion, processing, analytics, reporting, monitoring, and backend application workloads.

Developed scalable data ingestion and processing workflows using AWS Lambda, Amazon S3, DynamoDB, Amazon Redshift, and Boto3 to support telecom event processing, subscriber activity analytics, and enterprise reporting.

Developed Kafka-based event-driven messaging services to process subscriber events, provisioning requests, network notifications, and operational data across distributed telecom platforms.

Deployed and managed containerized telecom applications on Kubernetes clusters to improve scalability, resiliency, and operational efficiency.

Designed scalable cloud integration patterns using API Gateway, SQS, EventBridge, Lambda, serverless ETL pipelines, asynchronous processing, retry mechanisms, and fault-tolerant service communication across distributed telecom applications.

Developed browser automation and end-to-end validation workflows using Playwright and Python for telecom web applications.

Integrated enterprise data across MySQL, DynamoDB, Amazon Redshift, and Redis to support transactional processing, subscriber analytics, operational reporting, and cloud-native backend services across distributed telecom applications.

Built scalable batch and event-driven data processing services using Python, AWS Lambda, Amazon S3, DynamoDB, Amazon Redshift, and Boto3 to automate telecom operational workflows and enterprise analytics.

Containerized and deployed backend services using Docker, Jenkins, Git, GitHub, AWS CodePipeline, Linux, Gunicorn, Nginx, and Uvicorn, supporting automated CI/CD pipelines, production deployments, and environment promotion.

Built API and data integrations connecting backend applications, databases, cloud platforms, messaging systems, analytics services, and downstream enterprise applications.

Developed scalable AWS processing workflows using Lambda, Boto3, S3, DynamoDB, Redshift, API Gateway, SQS, EventBridge, and CloudWatch.

Containerized and deployed Python backend services using Docker and Kubernetes with automated CI/CD pipelines for scalable and reliable production deployments.

Supported production monitoring, debugging, troubleshooting, performance optimization, and root-cause analysis using CloudWatch, Linux logs, Postman, Wireshark, Fiddler, and enterprise monitoring tools.

Collaborated with engineering, data, QA, DevOps, cloud, infrastructure, product, and business teams throughout Agile development and production delivery.

Implemented secure telecom data handling practices using IAM-based access control, RBAC, audit logging, encrypted API communication, and enterprise security standards for customer and network operational data.

Collaborated with frontend teams to integrate backend APIs with React.js, Angular, JavaScript, jQuery, AJAX, HTML5, CSS3, and JSON, enabling enterprise dashboards for subscriber management, network performance monitoring, service requests, operational analytics, and reporting.

Participated in Agile/Scrum ceremonies, sprint planning, technical design discussions, code reviews, defect triage, production support, and cross-functional collaboration with DevOps, QA, Data Engineering, Cloud, Network Operations, and Product teams.

Environment: Python, Django, Django REST Framework (DRF), FastAPI, REST APIs, Microservices, Pandas, NumPy, Scikit-learn, Celery, Redis, MySQL, DynamoDB, Amazon Redshift, AWS Lambda, Amazon EC2, Amazon S3, API Gateway, Amazon SQS, EventBridge, IAM, VPC, CloudWatch, Docker, Jenkins, Git, GitHub, AWS CodePipeline, Terraform, AWS CloudFormation, Gunicorn, Uvicorn, Nginx, React.js, Angular, JavaScript, jQuery, AJAX, HTML5, CSS3, JSON, SQL, Postman, Wireshark, Fiddler, Linux, Agile/Scrum.

Citi bank, New York, NY

Aug 2021 – Apr 2023

Python Developer

Designed and deployed a real-time PnL tracking platform for equities and fixed income trading desks, giving traders accurate intraday visibility into profitability, including fees, hedging adjustments, liquidity costs, and currency exposure.

Developed highly scalable FastAPI microservices that consumed high-frequency trade and market events from Kafka, calculated low-latency PnL and risk figures, and delivered normalized outputs to analytics dashboards and trading tools.

Integrated live data feeds from Bloomberg and Tradeweb, ensuring consistent pricing, yield curves, and liquidity metrics across fixed-income products and enhancing risk model accuracy.

Developed frontend components using HTML5, XHTML, CSS3, JavaScript, AJAX, XML, and DHTML to support interactive banking screens and internal user-facing application features.

Designed and implemented Django-based web application modules by integrating backend services, database-driven workflows, templates, views, forms, and dynamic user interfaces.

Created robust market data normalization utilities using Pandas and NumPy, improving calculation consistency and reducing latency across risk workflows.

Connected Databricks, EMR, and Snowflake to support historical trade data analysis for VaR, stress testing, and compliance reviews, enabling deeper insights and improved audit readiness.

Engineered high-volume ETL flows moving intraday trades from Kafka into PostgreSQL and Snowflake, supporting near real-time portfolio and liquidity analytics.

Designed event-driven architectures using Kafka producers and consumers to process high-frequency trade events, portfolio updates, pricing feeds, and downstream risk analytics.

Implemented end-to-end Snowflake ingestion and transformation pipelines, ensuring clean, validated transactional data for compliance, risk modeling, and analytics teams.

Automated regulatory reporting using Python, SQL, Tableau APIs, and Pandas-based data transformations to reduce reporting errors and support audit readiness.

Automated ETL, compliance, and reconciliation workflows using AWS Step Functions and Airflow, ensuring predictable scheduling, improved reliability, and easier operational monitoring.

Collaborated closely with traders and quants to design REST APIs exposing portfolio deltas, liquidity indicators, and risk metrics, directly powering internal trading applications.

Optimized SQL queries for reporting, search, validation, transaction review, and backend data-processing workflows.

Strengthened API security using IAM, OAuth2, JWT, RBAC authentication, encryption standards, and centralized audit logging across all trading and PnL endpoints.

Worked with Compliance and DevOps teams to implement governance controls for PnL data, enabling full traceability, encryption, and secure data lineage across the platform.

Provisioned infrastructure using Terraform and Ansible, ensuring consistent provisioning practices across dev, QA, and production clusters.

Designed asynchronous FastAPI services using asyncio and Uvicorn to process concurrent trade events with low latency.

Configured and tuned Uvicorn/Gunicorn workers for FastAPI to maximize throughput, stability, and latency performance during periods of extreme market volatility.

Deployed containerized services to AWS EKS through Terraform and Jenkins CI/CD pipelines, ensuring consistent builds, reproducible environments, and secure production deployments.

Established full observability using Prometheus metrics, CloudWatch dashboards, and alerting to proactively detect latency spikes, delays, and error conditions.

Tuned EKS autoscaling and cluster capacity based on trading workload patterns, improving cost efficiency while maintaining system resiliency.

Delivered a production-grade, mission-critical PnL platform that significantly improved transparency, strengthened operational controls, and supported global compliance obligations.

Collaborated with quants to integrate predictive ML models into real-time PnL and risk pipelines, helping traders detect abnormal market behavior earlier.

Built automated feature-engineering utilities with Pandas/NumPy to support ML-based exposure and stress testing workflows.

Collaborated with business analysts, QA teams, SMEs, architects, and banking stakeholders to validate requirements, support UAT, troubleshoot defects, and deliver production-ready application components.

Environment: Python, FastAPI, Django, Django REST Framework, REST APIs, Microservices, Kafka, Pandas, NumPy, PostgreSQL, Snowflake, Amazon EMR, Databricks, AWS EKS, Amazon S3, AWS Step Functions, IAM, CloudWatch, Docker, Kubernetes, Terraform, Ansible, Jenkins, Airflow, Git, GitHub, Gunicorn, Uvicorn, Prometheus, Tableau APIs, Bloomberg API, Tradeweb API, HTML5, CSS3, JavaScript, AJAX, XML, JWT, OAuth2, RBAC, SQL, Agile/Scrum.

Molina Healthcare Inc., Long Beach, CA

Jan 2020 – July 2021

Python Developer

Designed and developed end-to-end healthcare predictive analytics platform components using Python, Django, Django REST Framework, FastAPI, REST APIs, Microservices, MySQL, DynamoDB, and Amazon Redshift to support suicide-risk prediction and care-management workflows.

Built Django ORM models, business logic layers, and REST API endpoints to manage member profiles, clinical indicators, behavioural health records, claims data, risk-score outputs, outreach status, referral workflows, and follow-up activities.

Developed FastAPI-based lightweight services using Pydantic for low-latency risk-score retrieval, data validation, and service-to-service communication across ML, backend, and analytics workflows.

Implemented Azure DevOps release pipelines for Python services, including build validation, automated test execution, environment-specific configuration, artifact promotion, and controlled deployment across development, QA, and production environments.

Developed Azure Functions and Azure Storage integrations for scheduled reconciliation and reporting jobs, using secure connection settings, structured logging, and failure notifications to improve operational visibility and reduce manual support effort.

Developed Snowflake Data Cloud integration workflows using Python, SQL, Snowflake Connector, staged file loads, COPY INTO, and transformation queries to support claims reporting, reconciliation, and analytics datasets.

Data ingestion: Developed Dataproc PySpark jobs for large-scale telemetry processing and integrated Cloud Storage with BigQuery pipelines for centralized analytics and compliance reporting.

Security automation: Implemented Qualys FIM across Linux virtual machines, containerized workloads, and auto-scaled compute environments to detect configuration drift and support enterprise vulnerability management.

Testing & security: Authored BDD test suites in PyTest + Gherkin with 85%+ coverage; all secrets managed through HashiCorp Vault.

Implemented automated workflows using Quartz Scheduler for scheduled report generation, recurring notifications, policy synchronization, document processing, support queue updates, recurring data refreshes, and end-of-day operational tasks.

Deployed backend applications on Microsoft Azure using Azure Virtual Machines, Azure App Services, and Azure Storage to support secure, scalable, and reliable enterprise healthcare application deployments.

Developed ETL and reporting workflows integrating Azure-hosted enterprise applications with GCP analytics platforms to support operational reporting, historical data analysis, and business intelligence solutions.

Developed Event Management workflows for tracking logs, audit events, application errors, operational alerts, and generating reports in PDF, HTML, and Excel formats for business and compliance teams.

Developed distributed communication layers using TCP/IP, UDP, and ZeroMQ to support inter-service communication and backend workflow integration across enterprise applications.

Containerized Python backend services using Docker for consistent application deployment across development, QA, and production environments.

Deployed containerized healthcare services on Kubernetes clusters to improve scalability and high availability.

Utilized Git and GitHub for version control, collaborative development, release management, and source code tracking across distributed teams.

Wrote unit test cases using Python UnitTest and performed testing, troubleshooting, production support, query analysis, root cause analysis, and performance optimization activities while working in Agile (Scrum) and complete SDLC environments.

Environment: Python, Django, Django REST Framework (DRF), FastAPI, REST APIs, Microservices, MySQL, DynamoDB, Amazon Redshift, Snowflake, Azure DevOps, Azure Functions, Azure App Service, Azure Virtual Machines, Azure Storage, PyTest, Quartz Scheduler, Git, GitHub, Linux, TCP/IP, UDP, ZeroMQ, Pandas, SQL, JSON, Docker, Kubernetes, Agile/Scrum.

Neon Software Services, Hyderabad, India

Sep 2017 – Nov 2019

Python Developer

Participated in requirement gathering and analysis phase of the project in documenting the business requirements by conducting meetings with various business users.

Developed and deployed Python/Django application components in Microsoft Azure, using Azure App Service, Azure Storage, and Azure SQL integration to support insurance dashboard workflows, document handling, and secure policy-data access.

Supported Azure-based release and operational activities through Azure DevOps pipelines, environment-specific configuration, application logging, and monitoring to improve deployment consistency and production support.

Developed UI components using HTML5, CSS3, JavaScript, AJAX, Bootstrap, XHTML, and DHTML to support dynamic data visualization, interactive reporting screens, and internal business-user workflows.

Integrated RESTful/SOAP web services and handled JSON-based data exchange for external system integration and application-level communication.

Automated daily and monthly PDF report generation using Aspose PDF, reducing manual reporting effort and improving report consistency.

Designed MySQL database schemas and developed Django ORM models while writing optimized SQL queries for MySQL and Oracle databases.

Developed AWS-based ETL workflows using S3, Lambda, and SQL to integrate heterogeneous insurance data into centralized reporting systems.

Implemented responsive HTML5/CSS3/jQuery/AJAX frontends; parsed JSON and XML from external REST APIs; worked in Agile SDLC with business users, QA teams, and cross-functional stakeholders through full delivery lifecycle.

Wrote and modified Oracle SQL queries for data retrieval, data updates, and backend processing while gaining experience with Stored Procedures, Triggers, and database consistency validation workflows.

Gained exposure to messaging systems such as Apache Kafka and IBM MQ, assisting in monitoring message flows, asynchronous communication, and enterprise integration workflows. Utilized PyQuery for HTML parsing, data extraction, and backend automation support activities.

Environment: Python, Django, Django ORM, HTML5, CSS3, Bootstrap, JavaScript, jQuery, AJAX, JSON, XML, REST APIs, SOAP Web Services, MySQL, Oracle, Azure App Service, Azure SQL Database, Azure Storage, Azure DevOps (VSTS), AWS S3, AWS Lambda, Aspose PDF, Apache Kafka, IBM MQ, PyQuery, Git, GitHub, Jenkins, Linux, SQL, Agile/Scrum.

TECHNICAL SKILLS

Programming Languages: Python, SQL, JavaScript, TypeScript, PowerShell

Python Frameworks: Django, Django REST Framework (DRF), FastAPI, Flask, SQLAlchemy, Pydantic

AI/Machine Learning: OpenAI API, LangChain, Retrieval-Augmented Generation (RAG), Prompt Engineering, FAISS, Vector Search, Semantic Search, Embeddings, Scikit-learn, Pandas, NumPy, PySpark

Frontend Technologies: React.js, Angular, HTML5, CSS3, Bootstrap, JavaScript, jQuery, AJAX, JSON, XHTML, DHTML

API & Microservices: RESTful APIs, SOAP Web Services, Microservices, API Gateway, AsyncIO, Uvicorn, Gunicorn

Databases: PostgreSQL, MySQL, Oracle, MongoDB, DynamoDB, Redis, Snowflake, Amazon Redshift

AWS Cloud: EC2, S3, Lambda, API Gateway, RDS, DynamoDB, IAM, VPC, CloudWatch, Athena, SQS, EventBridge, Step Functions, EKS, CodePipeline, CloudFormation

Microsoft Azure: Azure App Service, Azure Virtual Machines, Azure Functions, Azure Storage, Azure SQL Database, Azure Active Directory, Azure DevOps, Azure Pipelines, Azure Repos, Azure Key Vault, Azure Monitor

Google Cloud Platform (GCP): Google Compute Engine (GCE), Google App Engine (GAE), BigQuery, Dataproc, Cloud Storage, Cloud IAM, Cloud Logging, Cloud Monitoring

DevOps & CI/CD: Docker, Kubernetes, Jenkins, GitHub Actions, CircleCI, Bamboo, Terraform, AWS CloudFormation, Ansible, Git, GitHub

Data Engineering & ETL: ETL Pipelines, Apache Airflow, Amazon EMR, Databricks, AWS Athena, PySpark, SQL Optimization, Data Transformation

Messaging & Streaming: Apache Kafka, Amazon SQS, Amazon EventBridge, IBM MQ, ZeroMQ

Testing & Automation: PyTest, PyUnit, Playwright, Postman, Unit Testing, Integration Testing, API Validation, Regression Testing, User Acceptance Testing (UAT)

Monitoring & Logging: AWS CloudWatch, Splunk, New Relic, Prometheus, Linux Logs, Wireshark, Fiddler

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