Experience
$ srimanth --experience --sort=recent
aws-partner-sa.sh
$ role
Partner Solutions Architect/AI Acceleration Architect
$ period
August 2024 — Present
$ location
New York, NY
$ highlights
> Discovered and developed an AI-native startup through the technical review and discovery process, resulting in a Marketplace listing and elevated partner status that facilitated significant ARR and active pipeline growth
> Influenced a federal agency to commit to a digital twin opportunity by co-developing and delivering a simulation workshop at a government research facility, showcasing a pipeline utilizing GenAI agents powered by AWS Bedrock and Claude — with potential to scale across multiple facilities
> Discovered a renewable energy AI platform startup through the AWS technical review process, leading to multiple Marketplace listings and active pipeline — currently driving progression toward deeper co-sell status and advising on their architecture evolution to AWS-native AI services
> Orchestrated a blockchain data provider's infrastructure modernization from ECS to EKS, redesigning their monolithic architecture to enable independent scaling of core services and optimized costs
stack
AWS CloudGenerative AI - AI/MLMCP ProtocolClaude CodePythonTypeScriptServerlessWeb3Spatial ComputingIoTKubernetes/EKS
$ metrics
> Partners Engaged: 20+
> Technical Validations: 70+
> Web3 Partners Onboarded: 12 (2.4x goal)
+ 6 more metrics
aws-intern.sh
$ role
Solutions Architect Intern
$ period
May 2023 — August 2023
$ location
New York, NY
$ highlights
> Designed an end-to-end conversational AI platform with an Unreal Engine MetaHumans frontend powered by a serverless Python backend — lifelike AI avatars conducted real-time NLP-driven customer interactions in a virtual retail environment
> Implemented semantic search and RAG pipelines powered by LLMs (Falcon 40B, Claude, OpenAI embeddings) to generate context-aware responses, optimizing retrieval strategies to improve answer accuracy
> Built the full serverless infrastructure on AWS Lambda, creating a production-ready architecture that demonstrated enterprise AI capabilities to internal stakeholders
> Improved the partner discovery process by leveraging Crunchbase data to identify high-growth startups and high-potential partners, enabling the team to prioritize outreach based on funding stage, growth signals, and market fit
stack
PythonServerlessLLMsRAGClaudeFalcon 40BOpenAI EmbeddingsUnreal EngineMetaHumansAWS LambdaCrunchbase
$ metrics
> Llms Integrated: 3 (Falcon 40B, Claude, OpenAI)
> Architecture: Serverless + Unreal Engine MetaHumans
> Focus: Conversational AI, Semantic Search, Partner Discovery
rsk-analytics.sh
$ role
Data Scientist
$ period
October 2023 — August 2025
$ location
Remote
$ highlights
> Engineered end-to-end NBA and NFL analytics pipelines using Python, Pandas, and AWS Lambda/ECS — aggregated player and team data from external APIs and proprietary datasets for real-time analysis
> Developed NBA player projection models incorporating pace, defense vs. position, and cluster-based team tendencies to generate context-aware forecasts
> Built DFS simulation and projection scripts automating daily updates with injury and rotation adjustments for DraftKings player pools
> Created NFL fantasy scoring and betting edge models integrating bookmaker odds, coverage metrics, and custom play-by-play features to identify high-value prop opportunities
stack
PythonPandasAWS LambdaAWS ECSStreamlitTableauClaudeNFL AnalyticsNBA AnalyticsMLB AnalyticsDFS
$ metrics
> Sports Covered: NFL, NBA, MLB
> Pipeline Type: End-to-end analytics
> Tools Built: Projections, DFS sims, betting models, dashboards
+ 1 more metrics
vanguard.sh
$ role
Software Engineering Intern
$ period
June 2022 — May 2023
$ location
Malvern, PA
$ highlights
> Built a custom feeder service using AWS Lambda, DynamoDB, and Node.js to integrate with a third-party task management API, handling attribution of 3,000+ task bubbles while pulling user data through Okta SSO — created a Centralized Workspace integrating 20+ tools that increased process efficiency by 25% and projected significant overtime cost savings over 5 years
> Optimized a Tableau dashboard for the division, enhancing product roadmap visualization, streamlining data filtering, and boosting data ingestion efficiency by 50% across 30+ projects
> Engaged directly with business stakeholders in "Day in the Life" sessions, capturing end-user needs and translating them into product alignment improvements
stack
PythonNode.jsAWS LambdaDynamoDBOkta SSOTableauREST APIsBackend Automation
$ metrics
> Tools Integrated: 20+
> Task Bubbles Managed: 3,000+
> Efficiency Gain: 25%
+ 3 more metrics
kollabio.sh
$ role
Software Engineer
$ period
December 2021 — July 2022
$ location
Ashburn, VA
$ highlights
> Developed 10+ REST API endpoints for a federal workforce management tool using TypeScript and LoopBack, with Postman-automated test suites for validation — designed an AI chatbot using Amazon Lex integrated with those APIs to automate user support and resolve inquiries end-to-end
> Partnered with the QA team and Scrum Master to ensure product quality, refining the backlog and aligning development with product objectives in an Agile structure — demonstrated cross-functional collaboration across engineering, QA, and federal stakeholders
stack
TypeScriptLoopBackAmazon LexREST APIsPostmanAWSAgile/ScrumFederal
$ metrics
> Client: U.S. Federal Agency
> Api Endpoints: 10+
> Focus: AI Chatbot + API Development + QA
uva-ta.sh
$ role
Teaching Assistant
$ period
September 2021 — May 2023
$ location
Charlottesville, VA
$ highlights
> Managed 400+ students across multiple CS courses (CS 2110, CS 2100, CS 3240), leading lab sessions and ensuring effective instructional delivery
> Oversaw project groups for CS 3240 (Advanced Software Development), conducting weekly sprint meetings to maintain project timelines and outcomes in an Agile structure
> Dedicated 60+ hours to office sessions, addressing software development queries and fostering hands-on learning
> Guided Darden MBA students (GBUS 8640) in app development, teaching Python, jQuery, Firebase, and WordPress to a non-technical audience
stack
PythonjQueryFirebaseWordPressAgile/ScrumTechnical Instruction
$ metrics
> Students Managed: 400+
> Courses: CS 2110, CS 2100, CS 3240, GBUS 8640
> Office Hours: 60+
+ 1 more metrics
uva-research.sh
$ role
Undergraduate Research Assistant
$ period
2021 — 2022
$ location
Charlottesville, VA
$ highlights
> Developed a rumor detection algorithm to curb COVID-related misinformation, building a Bi-Directional Graph with 4,000+ data points using Python and the Twitter API as the foundation for training the ML model
> Transformed 5 distinct datasets, optimizing them for the Fairness AI Datasets family using Excel and Jupyter Notebook
> Leveraged the Twitter API to generate and refine datasets pivotal for the algorithm's training and evaluation
> Initiated a smart buildings research project with Professor Hongning Wang, laying the framework for next-generation building intelligence
stack
PythonJupyter NotebookTwitter APIMachine LearningGraph AlgorithmsNLPIoTExcel
$ metrics
> Data Points: 4,000+
> Datasets Transformed: 5
> Research Areas: Misinformation Detection, Smart Buildings