Shashank Srivastava

- Developed AI-assisted workforce scheduling features using React.js, Python, REST APIs, and AWS services
(S3, Lambda), optimizing task allocation across workers, resources, and production schedules.
- Built modern dashboards with React.js, Tailwind CSS, reusable component architecture, and real-time analytics while
modernizing legacy planning applications.
- Designed Python ETL pipelines, integrated AI-powered workflow automation, and improved performance
through lazy loading, API optimization, efficient state management, and scalable frontend architecture.

- Built an AI-powered workforce scheduling platform using React.js, TypeScript, Node.js, AWS, and
PostgreSQL to automate production planning across workers, shifts, and resource availability.
- Architected React-based scheduling dashboard using TypeScript and PostgreSQL APIs, supporting 500+ daily
scheduling operations while reducing planner workload by 80%.
- Integrated multiple LLMs including Anthropic Claude, OpenAI GPT, and Mistral via Amazon Bedrock,
implementing rule-based model routing to dynamically select the most effective model based on task complexity and
business requirements.
- Reduced dependency on 5 - 10 supply-chain planners by automating workforce allocation recommendations,
significantly decreasing manual scheduling effort and operational overhead.
- Designed event-driven ETL architecture using AWS Lambda, S3, and SQS to synchronize daily construction
datasets with downstream IRIS services through incremental change detection.

- Developed production-grade analytics platforms using React.js, Next.js, AG Grid, and data visualization libraries for
global mining and metals industry customers.
- Built reusable component-driven architectures and advanced analytical dashboards that reduced manual business
calculations and reporting efforts by 1080+ hours annually.
- Implemented complex filtering, aggregation, and visualization workflows for large datasets involving metal
production, commodity analysis, and manufacturing insights, improving user engagement by 40%.
- Improved application performance through code splitting, lazy loading, memoization, and optimized rendering strategies,
resulting in 20% faster load times.
- Achieved 90% Jest test coverage, resolved critical SonarQube issues, improved code quality standards, and increased
platform conversion rates by 15%.

- Engineered a reusable micro-frontend automation utility adopted across 60+ healthcare customer environments,
eliminating repetitive annual configuration updates and saving 30+ engineering hours every week.
- Led modernization of enterprise MIPS and MVP dashboards used by healthcare providers participating in CMS
incentive programs, improving UI consistency, usability, and performance.
- Optimized large-scale dashboard rendering, API integrations, and data-loading workflows, enabling operations teams to
process clinical requests 30% faster.
- Collaborated with product owners, QA teams, and stakeholders to deliver dashboard enhancements, reporting
capabilities, and workflow automation features across multiple healthcare clients.
- Created onboarding documentation, architecture guides, and technical walkthroughs for new engineers,
reducing onboarding time and mentoring a team of 4+ developers on platform architecture and business workflows.

- Improved dashboard usability and data visibility UI, enabling operations teams to process clinical requests 30% faster.
- Developed data-driven dashboard features by integrating backend APIs and Python-based data processing services,
improving operational visibility and analytics.

- Built an enterprise data hub platform using Starburst Galaxy, integrating data from AWS S3, PostgreSQL, and
Python ETL pipelines, while enhancing data discoverability through Collibra Search API.
- Improved large-scale data visualization by optimizing React data flows, developing scalable UI components, and
collaborating with backend teams to integrate and validate Python-based REST APIs.

- Built an enterprise data marketplace platform using React.js, Starburst Galaxy, AWS S3, PostgreSQL, and
Python ETL pipelines, enabling self-service data discovery and consumption across business units.
- Architected frontend data-loading pipeline supporting 1M+ records, reducing page rendering time by 40% through
virtualization, memoization, server-side pagination, and API optimization.
- Integrated Collibra Search APIs, improving enterprise-wide data discoverability, metadata management, and catalog
search experiences across 500K+ datasets.
- Implemented asynchronous data-export workflows using AWS SQS, cloud storage, and event-driven
notifications, providing real-time download status updates and significantly improving user experience.
- Partnered with architects and senior leadership during platform design discussions, contributing to cloud
architecture decisions involving AWS services, storage strategies, and distributed data workflows.
- Received client recognition for delivering highly adopted platform features, improving data accessibility, operational
efficiency, and overall user satisfaction.