Srivatsa Gadicherla

• •Built and deployed a production AI-powered contact center platform using AWS (Amazon Connect, Lex V2, Lambda, Bedrock, S3, CloudWatch), supporting 24/7 customer service and reducing average customer wait times by 60–80%.
• •Developed Retrieval-Augmented Generation (RAG) pipelines integrating vector search and enterprise knowledge bases, improving response accuracy and reducing hallucinations using Claude, Amazon Titan, and LLM validation layers.
• •Designed and implemented backend services and REST APIs for call routing, conversation state management, analytics ingestion, and model orchestration using Python and AWS Lambda.
• •Implemented system observability and monitoring using AWS CloudWatch, Glue, Athena, and QuickSight, enabling real-time performance tracking and analytics dashboards.
• •Automated infrastructure provisioning using Infrastructure-as-Code (AWS CloudFormation) and implemented CI/CD pipelines for deployment validation and rollback safety.
• •Collaborated cross-functionally with product managers, ML engineers, and DevOps teams to deliver scalable cloud-native architecture supporting high concurrency workloads.
• •Authored technical architecture documentation and presented system design and business impact analysis to senior engineering leadership and stakeholders.

• •Designed, deployed, and maintained cloud-native backend infrastructure on AWS (EC2, IAM, VPC, Route 53, S3, ECS), supporting AI-powered production systems with high availability, scalability, and security.
• •Built and optimized backend services using Java (Spring MVC) and Python, implementing data validation, business logic, and microservice-based workflows to support real-time and batch inference pipelines.
• •Developed and maintained REST APIs for internal tooling, analytics ingestion, and model orchestration across distributed services.
• •Implemented CI/CD deployment workflows and container-based deployments using AWS ECS and Docker to reduce release time and improve system reliability.
• •Designed and deployed a relational database system to replace Confluence-based workflows, reducing data retrieval times by 60% and improving operational efficiency across engineering teams.
• •Built full-stack internal dashboards using JavaScript, HTML, Tailwind CSS, Node.js, and React, improving access to critical resources and increasing team productivity by 30%.
• •Developed Python-based batch processing pipelines for large-scale datasets using numerical optimization techniques (Black–Scholes modeling and Newton–Raphson method) to improve predictive accuracy and computational performance.
• •Automated ETL workflows to normalize and process large CSV and Excel datasets, improving data consistency and reducing manual processing overhead.
• •Collaborated with AI researchers, backend engineers, and DevOps teams to deliver scalable AI infrastructure supporting multi-tenant workloads.

AWS Solutions Architect - Associate

Amazon Web Services

Cloud & Infrastructure

AWS (EC2, Lambda, S3, CloudWatch, ECS, IAM, VPC, Route 53, Amazon Connect, Lex V2, Bedrock, Glue, Athena, QuickSight, CloudFormation) • Docker • CI/CD • Infrastructure-as-Code

Languages

Python • Java • JavaScript • TypeScript • SQL

Frameworks & Libraries

React • Next.js • Node.js • Spring MVC • Tailwind CSS

AI/ML

RAG (Retrieval-Augmented Generation) • Vector Search • LLM Integration (Claude, Amazon Titan) • Batch Processing • Numerical Optimization

Tools & Technologies

REST APIs • Microservices • ETL Pipelines • Database Design • System Observability • Analytics Dashboards