Nitish Naidu Kandi

Project: AI/ML Model Hosting & Self-Serve Platform Portal

• Contributed to coding, testing, supporting, and debugging for an AI/ML model hosting platform portal, fostered quality and best practices across the team, and built Python application services enabling modeling teams to host AI/ML models as APIs in an easy, self-serve, safe, and performant way.
• Developed comprehensive unit tests, integration tests, and developer testing to ensure deliverable quality, performed technical analysis to solve reported platform issues, and demonstrated excellent problem-solving and debugging skills maintaining platform reliability across all AI/ML model hosting workflows.
• Designed and built serverless and stateful systems on AWS using Lambda, ECS, S3, DynamoDB, API Gateway, Step Functions, SQS, IAM, and Security Groups that were scalable and highly available, and applied Terraform IaC for automated infrastructure provisioning across development, staging, and production environments.
• Developed React, JavaScript/TypeScript, Redux, HTML, and CSS UI interfaces for the AI/ML platform portal, integrated Snowflake for data access and analytics, applied microservice development patterns, and configured and deployed CI/CD pipelines using GitLab for automated platform delivery.
• Applied Apigee for API management and gateway configuration, worked independently to define, manage, and complete technical activities while meeting project timelines and budgets, and demonstrated AI/ML experience in building and optimizing model hosting infrastructure.
• Worked effectively in an Agile environment, maintained version control using GitLab workflows, and communicated clearly across technical and non-technical stakeholders ensuring all engineering decisions were transparent, well-documented, and aligned with platform roadmap priorities.
• Demonstrated strong 5+ years of Python application service development expertise, contributed to platform-wide quality and best practice adoption, and built scalable, highly available AWS-native infrastructure that enabled organization-wide self-serve AI/ML model deployment at enterprise scale.

Project: Apex Protocol

• Contributed to coding, testing, and debugging Python application services and React UI interfaces for an AI/ML-enabled financial platform, developed comprehensive unit and integration tests ensuring deliverable quality, and performed technical analysis to solve reported issues across all stack layers.
• Designed and built serverless and stateful systems on AWS using Lambda, ECS, S3, DynamoDB, API Gateway, Step Functions, SQS, and IAM serving 50,000+ daily users, applied Terraform IaC for scalable infrastructure provisioning, and maintained 99.5% platform availability.
• Developed React, TypeScript, Redux, HTML, and CSS UI interfaces for the platform portal, integrated Snowflake for data access and analytics, applied microservice development patterns, and configured CI/CD pipelines using GitLab for automated build and deployment workflows.
• Applied Apigee for API management, demonstrated strong Python application service development experience, worked independently to define and complete technical activities within timelines and budgets, and fostered quality and best practices across the full-stack engineering team.
• Worked in an Agile environment, maintained GitLab version control workflows, demonstrated AI/ML platform experience, and communicated clearly with technical and non-technical stakeholders delivering transparent, well-documented engineering decisions throughout all development cycles.
• Demonstrated strong problem-solving and debugging skills across distributed AWS systems, applied Security Groups and IAM access controls, and consistently delivered scalable, highly available serverless AWS infrastructure supporting AI/ML model hosting and enterprise analytics platform operations.

Project: Serverless and Scalability

• Contributed to Python application service and React UI development, wrote comprehensive unit and integration tests, and performed technical analysis to solve reported issues across serverless AWS-based platform deployments in 12 production projects.
• Built serverless AWS systems using Lambda, ECS, DynamoDB, API Gateway, Step Functions, and SQS, applied Terraform IaC for infrastructure provisioning, integrated Snowflake for data access, and configured GitLab CI/CD pipelines for automated delivery workflows.
• Developed React, TypeScript, Redux, HTML, and CSS UI interfaces, applied microservice development patterns, leveraged Apigee API management, worked in Agile environments, and demonstrated strong problem-solving, debugging, and independent technical execution skills.
• Fostered quality and best practices across the team, worked independently to define and complete technical activities within timelines, maintained GitLab version control, and communicated clearly with technical and non-technical stakeholders across all full-stack engineering deliverables.
• Applied AI/ML platform development experience, demonstrated strong Python application service development capabilities, and consistently contributed to scalable, highly available AWS-native platform infrastructure with strong self-governance and technical accountability throughout.