Sai Abhishek
Fairfax
Summary
Serverless Developer with over 7+ years of demonstrated experience in software development, specializing in Node.js full-stack solutions and cloud-based environments. Strong expertise in building and monitoring microservices, event-driven architectures, and serverless applications using AWS Lambda, SNS/SQS, S3, DynamoDB, and related services. Proficient in CI/CD pipelines with Jenkins and infrastructure as code. Experienced in distributed architectural design patterns, automated testing, and Agile methodologies.
Overview
8
8
years of professional experience
1
1
Certification
Work History
Cloud MLOPS Engineer
Gateway Solutions
Overland Park
06.2025 - Current
- Architected with Angular and implemented a highly scalable, fault-tolerant real-time data platform using Apache Kafka as the core event-streaming layer, integrating Kafka Connect for multi-source log ingestion and KSQLDB / Apache Spark Structured Streaming for near–real-time analytics. Successfully modernized a legacy batch-processing system, reducing alert latency from hours to seconds while improving throughput, reliability, and system observability.
- Designed and embedded machine learning–driven anomaly detection into streaming workflows using Isolation Forest, clustering techniques, and statistical models, enabling proactive detection of abnormal system behavior and user activity. Replaced static, rule-based alerting with an adaptive, intelligent monitoring framework, significantly reducing false positives and improving operational awareness.
- Developed and optimized large-scale Python and PySpark data pipelines to automate log parsing, enrichment, normalization, and validation across heterogeneous sources, including application logs, network telemetry, and cloud audit trails. Implemented schema enforcement, data quality checks, and enrichment logic, while leveraging advanced SQL for complex joins, aggregations, and trend analysis in Snowflake and BigQuery to support analytics and reporting needs.
- Designed and implemented data-driven cloud applications leveraging both relational and NoSQL databases, including Amazon RDS (MySQL/PostgreSQL) for transactional workloads and DynamoDB/MongoDB for high-throughput, low-latency access patterns. Provisioned and managed database and application infrastructure as code using AWS CloudFormation and Terraform, ensuring consistent, repeatable deployments.
- Applied data science and machine learning fundamentals—such as feature engineering, statistical analysis, and model evaluation—to support analytics pipelines and intelligent automation use cases.
- Led infrastructure automation and cloud environment standardization on Azure using Terraform, defining networking, compute, security, and storage resources as code to ensure consistency, repeatability, and controlled change management across environments.
- Containerized distributed services using Docker and orchestrated deployments with Kubernetes, enabling high availability, rolling updates, and horizontal scalability.
- Deployed and operationalized machine learning models on Azure Kubernetes Service (AKS) with horizontal pod auto-scaling, health checks, and performance monitoring. Integrated Azure OpenAI APIs into internal observability and support tools to provide AI-assisted log interpretation, anomaly explanations, and contextual insights, reducing mean time to detection (MTTD) and mean time to resolution (MTTR) for production incidents.
- Designed and maintained enterprise-grade CI/CD pipelines using Jenkins and GitLab CI, automating build, test, and deployment workflows across development, staging, and production environments. Integrated unit tests, integration tests, data validation checks, and ML model performance gates, resulting in a 40% increase in deployment frequency and a 60% reduction in rollback incidents, while improving overall release confidence and system stability.
Lambda Serverless Developer
Walmart
01.2021 - 06.2025
- Led the modernization and migration of a healthcare data analytics platform to a fully serverless architecture on AWS, leveraging event-driven design to process high-volume patient and claims data in real-time. The platform enabled secure, scalable data ingestion, transformation, analytics, and API exposure for internal healthcare applications and reporting dashboards, achieving cost efficiency and auto-scaling capabilities.
- Collaborated with stakeholders to gather requirements and design event-driven, serverless solutions aligned with healthcare data processing needs, translating business needs into scalable microservices and Lambda-based architectures.
- Designed and implemented serverless data ingestion and processing pipelines using AWS Lambda, Amazon S3, SNS/SQS for messaging and queuing, and EventBridge for event routing, replacing legacy batch systems with real-time, asynchronous workflows.
- Built and monitored microservices using Node.js on AWS Lambda, including full-stack API development with API Gateway, DynamoDB for NoSQL storage, and RDS for relational data needs.
- Developed serverless functions in Node.js and Python for data transformation, validation, and feature engineering, integrating with AWS Glue and Athena for analytics on data stored in S3.
- Implemented infrastructure as code using AWS CloudFormation and AWS CDK to provision and manage serverless resources (Lambda, S3, DynamoDB, SNS/SQS, VPC, KMS, ALB, Route 53, Neptune) in a repeatable and version-controlled manner.
- Configured CloudWatch alarms, logs, metrics, and dashboards for proactive monitoring, alerting, and troubleshooting of Lambda functions and serverless components. Angular is proficiently used in the frontend development.
- Established robust CI/CD pipelines using Jenkins and AWS CodePipeline to automate testing, deployment, and promotion of serverless applications, incorporating unit tests, integration tests, and automated security scans.
- Leveraged AWS CLI extensively to automate and manage cloud operations across multiple environments, including provisioning and configuration of Lambda, S3, DynamoDB, RDS, SNS/SQS, CloudWatch, ALB/ELB, and IAM resources. Built reusable CLI-based scripts for deployment validation, log inspection, permissions auditing, and incident triage, significantly reducing manual operational effort and accelerating troubleshooting and environment setup.
- Designed and developed RESTful APIs and event-driven integrations using Node.js, Express.js-like patterns in Lambda, and API Gateway, supporting full-stack solutions with React/Angular frontends when needed.
- Applied distributed architectural design patterns (event sourcing, CQRS, strangler pattern) to decouple services and ensure fault tolerance and scalability in the serverless environment.
- Integrated security best practices including AWS KMS for encryption, least-privilege IAM roles, VPC endpoints, and ALB/Route 53 for secure traffic routing.
- Optimized cold starts, memory allocation, and concurrency settings in Lambda to achieve sub-200ms latency and reduce operational costs by leveraging provisioned concurrency where required.
- Performed automated testing (Jest/Mocha for Node.js, PyTest for Python) and implemented canary deployments to minimize risk during production updates.
- Worked in an Agile/Scrum environment, contributing to sprint planning, daily stand-ups, and team success by supporting cross-functional tasks as needed.
- Measured impact: Reduced infrastructure costs by 28% through serverless adoption, improved system scalability to handle peak loads without provisioning overhead, and decreased deployment time from days to minutes.
Cloud Engineer
Hitachi Vantara
Hyderabad
01.2018 - 07.2021
- Engineered a multi-AZ, fault-tolerant infrastructure using AWS VPC, EC2 Auto Scaling Groups, and Elastic Load Balancers. Designed and implemented custom auto-scaling policies based on predictive traffic patterns (holiday sales) and real-time metrics (CPU, network I/O), which reduced downtime by 35% and improved system flexibility by 40% during peak load events.
- Automated the entire provisioning process using Terraform, defining all resources—including VPCs, security groups, IAM roles, and launch templates—as code. This ensured environment consistency, repeatable deployments, and version-controlled infrastructure changes.
- Wrote optimized SQL queries for aggregations and feature extraction across distributed datasets (Snowflake / BigQuery).
- Led a cost-optimization deep-dive, resulting in a 15% reduction in monthly cloud spend. This was achieved by right-sizing EC2 instances and leveraging Reserved Instances/Savings Plans for predictable workloads.
- Implemented a CloudFront and S3 caching strategy that reduced origin server load and decreased global latency by 25%.
- Strengthened the security posture by defining least-privilege IAM roles and policies for EC2 instances, segmenting the network with public and private subnets, and ensuring all data in transit was encrypted.
- Integrated the auto-scaling infrastructure with the existing CI/CD pipeline (e.g., Jenkins, GitLab CI) to enable seamless blue-green deployments. Configured detailed CloudWatch Alarms and Dashboards for proactive monitoring and automated healing of the application stack.
- Applied strong object-oriented programming (OOP) and core computer science principles—including abstraction, encapsulation, SOLID design, and data structures—to design maintainable, scalable systems.
- Implemented distributed architectural patterns such as microservices, event-driven architecture, and asynchronous messaging to ensure fault tolerance and scalability.
- Practiced systems analysis and design using OOAD and UML diagrams to model application workflows and database schemas, while enforcing automated testing (unit, integration, and regression tests) and managing version control and code reviews through Bitbucket.
- Designed and secured distributed, API-driven services using Amazon Neptune for relationship and graph-based data modeling, enabling efficient traversal and dependency analysis across interconnected system entities.
- Implemented AWS KMS–based encryption for data at rest and in transit, enforced least-privilege IAM access, and configured Application Load Balancers (ALB) / Elastic Load Balancers (ELB) for secure traffic routing, high availability, and horizontal scalability across microservices.
Education
B.Tech - Computer Science
Jawaharlal Nehru Technological University
Hyderabad, Telangana, IndiaSkills
- C
- C
- Python
- Java
- JavaScript
- Ruby
- Bash
- Ruby on Rails
- HTML
- CSS
- Model-View-Controller
- Linux
- Nodejs
- Expressjs
- React
- Angular
- TypeScript
- Apache Express
- Flask
- Django
- Apache Kafka
- Apache Flink
- Apache Storm
- Apache Zookeeper
- Docker
- Kubernetes
- Azure
- AWS
- Jenkins
- Terraform
- Ansible
- EKS
- ECS
- Fargate
- Managed Kafka
- Glue
- Athena
- Redshift
- Secret Manager
- Parameter Store
- SSO
- Bit Bucket
- Argo CD
- Tekton
- MySQL
- Oracle
- PostgreSQL
- MongoDB
- MariaDB
- Redis
- JIRA
- Confluence
- Agile/Hybrid Technology
- SDLC
Certification
- Smart bridge IBM 2020 Hackathon Winner
- Certified Data Engineer (Google)
- Cisco certified Cyber Security Engineer
- Certified AWS Machine Learning Engineer
Projects
Early Stage Autism prediction using Machine Learning, Python, Scikit-learn, Keras, Deployment(Flask API), Developed a machine learning framework for early Autism Spectrum Disorder (ASD) prediction using UCI Adult Screening Dataset, achieving 98.2% accuracy with XGBoost & Neural Network models in Python (Scikit-learn, Keras), deployed via Flask API, outperforming baselines by 18%. Parkinson’s disease prediction using Machine Learning, Machine Learning, Model Evaluation, AWS Sage Maker, Developed a machine learning model to predict Parkinson's disease with 92% accuracy using vocal biomarkers and algorithms like Random Forest, SVM, and XGBoost. Serverless Gen AI chatbot, LLM, RAG, Azure Function Compute, Designed and built a RAG-based conversational chatbot using Azure OpenAI (GPT-4) for enterprise knowledge retrieval. Used Azure Cognitive Search + Embeddings API to index ~10K+ internal documents into a vector search index. Automated Transcription of Middle English Manuscripts, Machine Learning, Neural Networks, Employed OCR and clustering algorithms to segment words and letters in medieval English Manuscripts from the Piers Plowman Electronic Archives at MGIT for transcription. Graphics Bot, Node.js, Express, Chart.js, Botpress, GitHub REST API, Boosted developer productivity by 40% by creating 'GRAPHICS BOT,' a user-friendly GitHub ChatBot that extracts and visualizes key data from the GitHub API on issues, collaborators, and contributions. E-Commerce Platform with Microservices, Docker, Azure Cloud, Java, Architected scalable e-commerce platform using microservices architecture with Spring Boot, Angular 11 frontend, and Oracle database. Implemented containerized deployment using Docker and AWS cloud services. Developed comprehensive product catalog, shopping cart, and payment processing modules with secure REST API integration and Oracle PL/SQL backend procedures. Built responsive Angular components with TypeScript and implemented state management for seamless user experience across multiple device platforms.
Languages
- C
- C++
- Python
- Java
- JavaScript
- Ruby
- Bash
Timeline
Cloud MLOPS Engineer
Gateway Solutions
06.2025 - Current
Lambda Serverless Developer
Walmart
01.2021 - 06.2025
Cloud Engineer
Hitachi Vantara
01.2018 - 07.2021
B.Tech - Computer Science
Jawaharlal Nehru Technological University