Full Stack Python Developer

VISMAY PAREKH

****************@*****.*** New York, US

PROFESSIONAL SUMMARY

Full Stack and Cloud Engineer with 5+ years of experience building enterprise-scale platforms across financial services and large enterprise environments. Strong expertise in Python (FastAPI, Flask, Django, DRF) and React.js (TypeScript, Redux), developing high-performance RESTful APIs, microservices, and event-driven backend systems deployed on AWS (EC2, Lambda, S3, RDS, IAM, CloudWatch). Hands-on experience with AI and Generative AI, including LLM and GPT-based integration, LLM inference, Retrieval-Augmented Generation (RAG), vector embeddings, semantic search, prompt engineering, and machine learning pipelines. Experienced in predictive analytics, anomaly detection, AI-driven automation, and real-time data processing, with strong knowledge of distributed systems, ETL/ELT pipelines, SQL/NoSQL databases, containerization (Docker), CI/CD, and cloud-native application design. Proven ability to integrate Python backends with React-based frontends to deliver scalable, secure, and production-ready applications in Agile enterprise teams. PROFESSIONAL EXPERIENCE

Full stack Python Developer, IBM, (May 2024 – Present) Cloud-Native Observability & Automation Platform (SaaS) Designed and developed a backend-centric, cloud-native observability and automation platform using Python Programming

(FastAPI, Django, DRF) deployed on Amazon Web Services (AWS), leveraging AWS Lambda, event-driven architectures, Amazon S3, Amazon RDS, AWS CloudWatch, and serverless computing. The platform exposed high-performance RESTful APIs to ingest real-time telemetry data, support analytics, Artificial Intelligence and Machine Learning–driven anomaly detection, predictive analytics, and securely integrate with React.js dashboards (TypeScript, Redux). Implemented ML-assisted logic, AI-driven automation, intelligent decision systems, and machine learning pipelines to enable scalable, fault-tolerant monitoring and auto-remediation, improving system reliability and operational efficiency.

● Designed and implemented a backend-centric system architecture using Python, FastAPI, Flask, Django, asynchronous programming (async/await), microservices architecture, and modular service design, deployed on AWS EC2, ECS, containerized workloads on AWS (Docker), applying cloud-native application design, high availability, and fault tolerance.

● Developed high-performance RESTful API development in Python (FastAPI, Django, DRF) to support telemetry ingestion, analytics queries, user management, and AI-enabled insights, including (LLM, RAG, GPT-Based Model) model inference, feature engineering outputs, and secure frontend–backend integration with React.js, optimized for low latency.

● Built event-driven backend workflows using AWS Lambda, AWS Step Functions, message queues, and asynchronous Python services like Django, FastAPI to ingest, validate, transform, and enrich large volumes of real-time data, applying data preprocessing, data validation and serialization, and ensuring reliability, idempotency, and graceful failure handling.

● Designed and maintained relational and non-relational database schemas using Vector Embeddings, PostgreSQL, Redis, AWS RDS, and Amazon Redshift, applying normalization, indexing, partitioning, query optimization, and time-series modeling to efficiently store telemetry data, system metrics, audit logs, and AI/ML feature data.

● Optimized backend data access through SQL integration with Python, indexing strategies, vector caching strategies, and connection pooling, enabling fast analytics, AI-powered insights using LLM, RAG, GPT-Based Model, semantic and similarity search results, and scalable rendering for React-based dashboards.

● Implemented backend business logic LLM, RAG, and GPT-Based Model for AI-driven anomaly detection, predictive workflows, prompt engineering, intelligent alerting, MLOps, and rule-based automation, incorporating machine learning pipelines, model inference, and AI API integration to support proactive automated remediation.

● Built and maintained automated testing suites including unit testing, integration testing, prompt engineering and regression testing for Python services like Django, FastAPI and APIs, integrating quality checks into CI/CD pipelines on AWS, ensuring reliability, stability, model monitoring and observability, and release readiness. Python Developer, Bank of New York (BNY Mellon), (May 2020 – July 2023) Enterprise-Scale Data Lakehouse

Designed and built an enterprise-scale, Python-driven data platform and lakehouse deployed on Amazon Web Services (AWS), supporting real-time and batch data ingestion, ETL/ELT pipelines, analytics, and automation. The platform leveraged Python Programming (FastAPI, Flask, Django, Django REST Framework), distributed processing, SQL-based analytics, and Machine Learning pipelines to deliver curated datasets through RESTful APIs consumed by React.js dashboards. Incorporated Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI, including AI-assisted data validation, anomaly detection, predictive analytics, feature engineering, data preprocessing, model inference, AI-driven automation, and intelligent decision systems, ensuring scalability, reliability, and data quality.

● Designed and developed core backend system design and services using Python Programming, Object-Oriented Programming (OOP), asynchronous programming (async/await), and microservices architecture to power data ingestion, transformation, analytics, and AI-driven automation workflows, deployed on AWS cloud infrastructure for scalability

● Built and maintained ETL and ELT pipelines using Python-based orchestration, Amazon S3, and distributed processing, ingesting large volumes of structured and semi-structured data from APIs and enterprise systems with strong data preprocessing, data validation, and serialization.

● Developed high-performance RESTful API development using Python (FastAPI, Flask, Django, DRF) to expose curated datasets, analytics results, and AI-enhanced insights using (LLM, RAG, GPT-Based Model), enabling secure frontend– backend integration with React.js applications and downstream systems.

● Collaborated on React.js frontend dashboards using JavaScript (ES6+), TypeScript, Functional Components, React Hooks, Redux, Redux Toolkit, Context API, and asynchronous data fetching (Axios, Fetch API) to visualize KPIs, analytics trends, forecasts, and machine learning–driven metrics, improving data accessibility for business and executive stakeholders.

● Designed and optimized relational and analytical database schemas using PostgreSQL, SQL Server, Vector Embeddings, Amazon RDS, and Amazon Redshift, applying SQL integration with Python, query optimization, indexing, partitioning, and performance tuning to support high-throughput analytics and reporting.

● Implemented real-time and near-real-time data processing workflows and built machine learning–enabled Python pipelines for feature engineering, anomaly detection, predictive modeling, predictive analytics, and intelligent decision systems, supporting forecasting and risk analysis use cases.

● Developed and maintained automated testing frameworks including unit testing, integration testing, automated testing, MLOps and AI-assisted data validation checks for Python services like Django and FastAPI, prompt engineering, and pipelines, integrating quality checks into CI/CD pipelines to ensure data quality, reliability, and safe production releases.

● Applied cloud-native application design principles such as distributed processing, horizontal scalability, vector, high availability, fault tolerance, and observability, leveraging AWS CloudWatch logging and monitoring, alerting, and mentoring junior engineers to improve platform stability and SLA compliance. TECHNICAL SKILLS

Databases: SQL, PostgreSQL, MySQL, SQL Server, MongoDB, Redis, Amazon RDS, Amazon Redshift, Impala, Time-Series Data Modeling, Query Optimization, Indexing and Partitioning Programming Languages: Python, Java, C++, C#, JavaScript (ES6+), TypeScript, HTML/DOM, CSS3, R Tools and Frameworks: FastAPI, Django, Flask, Django REST Framework (DRF), ReactJS, Redux, Redux Toolkit, Spring Boot, Microservices Architecture, RESTful API Development, Event-Driven Architecture, Tableau, Postman, Selenium, Git, CI/CD Pipelines, Jenkins, Terraform, Ansible, SonarQube, Artifactory, Autosys (Job Scheduling), Docker, Kubernetes (EKS concepts), Continuous Testing, Release Automation, VS Code, PyCharm, Pandas, NumPy, Matplotlib, TensorFlow, PyTorch, scikit-learn, Machine Learning Pipelines, Generative AI Concepts, LLM Integration Concepts, AWS EC2, AWS S3, AWS Lambda, AWS RDS, AWS CloudWatch, AWS Step Functions, AWS CloudFormation, Cost Explorer, Datadog, Prometheus Certifications: AWS Certified Solutions Architect – Associate, AWS Certified Cloud Practitioner, Microsoft Azure Fundamentals (AZ- 900), Meta Front-End Developer

EDUCATION

State University of New York, Binghamton

Master of Science in Information Technology, May 2025

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