Vetted .NET Professionals

“End-to-end deployment owner for Oracle document delivery/print services in a hospital-like production environment, focused on reliability/performance at scale (thousands of systems). Also describes implementing event-driven RAG/agentic LLM workflows with attention to embeddings/index consistency, latency, and measurable improvements in response relevance and operational efficiency.”

“Serial entrepreneur with 15+ years in the VC, studio, and accelerator ecosystem who has founded multiple startups, raised capital previously, and built a consulting business running since 2008. Currently building a pre-seed SaaS marketplace for long-term housing in Texas with plans to expand across the U.S. and into Portugal, bringing a notably strategic focus on long-term market trends and exit planning.”

“Backend engineer with production experience on an AWS Lambda-based payment service (manually deployed) and hands-on modernization work using parallel-run + diffing to prove parity before cutover. Has practical production troubleshooting experience for batch/pipeline incidents using monitoring/logs and emphasizes idempotent rerunnable jobs for safe recovery; also improved GraphQL performance by refactoring overly broad queries.”

“Full stack engineer in the financial/thematic investing domain who built end-to-end applications on AWS. Notably redesigned a slow portfolio analytics workflow by offloading heavy computations to scheduled AWS Lambda jobs and caching results in DynamoDB (TTL), cutting API latency from ~5 seconds to under 300ms while supporting data-heavy daily market processing.”

“Enterprise Architecture leader with insurance domain experience (Farmers Insurance) who drove a multi-phase roadmap to modernize a siloed CRM landscape—migrating from legacy Siebel to Salesforce Financial Services Cloud with Customer 360, MDM, and omnichannel capabilities. Also led a high-impact architecture decision to implement offline billing to reduce customer-facing downtime, including complex SAP/on-prem-to-cloud integration and transaction sync.”

“Backend/platform engineer with experience across edtech, logistics, and AWS internal systems—owned a production course recommender end-to-end (model serving + APIs + caching/observability), delivering +30% CTR and -20% latency. Has scaled real-time delivery visibility/rerouting on Kubernetes/EKS to sub-200ms P95 during demand spikes and built billion-events/day telemetry pipelines on AWS (Kinesis Firehose, Lambda, S3, Redshift) with schema evolution, dedupe, and replay support.”

“Backend/LLM engineer who built a production-critical Amazon Bedrock + RAG correction and compliance layer for employee communications, integrating tightly with existing Spring Boot/AWS microservices to reduce manual review while keeping outputs explainable and auditable. Also designed an event-driven system processing 10M+ events/day (SQS/Lambda/DynamoDB/Elasticsearch) and handled on-call incidents with strong observability and reliability patterns (idempotency, retries, hotspot mitigation).”

Vidhi Upadhyay

Screened

Senior Software Engineer specializing in AI/ML, computer vision, and cloud-native systems

Remote8y exp

“Independently built a production-grade, containerized enterprise agentic AI platform (stateful orchestration + RAG) focused on real-world reliability—guardrails, citation-based outputs, reranking, query rewriting, and evaluation harnesses to reduce hallucinations. Hands-on with OpenAI SDK, CrewAI, and LangGraph, and has delivered AI solutions for non-technical NGO stakeholders via demos and practical POCs.”

“Backend/platform engineer building HIPAA-compliant, real-time healthcare systems: owned a Python/Flask API layer for an AI-enabled patient engagement and risk scoring service, implemented PHI-safe logging and cross-service auditability, and delivered Kubernetes microservices via ArgoCD GitOps. Also has experience with Kafka streaming pipelines and hybrid cloud-to-on-prem migrations in regulated healthcare/fintech environments.”

“Associate Director in commercial technology leading Salesforce platform delivery (Sales Cloud + Health Cloud) for patient engagement and order management. Personally led secure integrations like Bartender Cloud label/barcode generation (PDF creation, encryption, malware scanning) and owned a major StreamSets-based Salesforce data sync incident triggered by a Salesforce region move, adding proactive monitoring and automated DR/failover. Experienced in scaling delivery via CI/CD, release cadence, and leading teams through architecture reviews, code reviews, and lead-to-cash automation.”

“Built and shipped a production LLM-powered multi-agent system that autonomously generates and publishes YouTube videos end-to-end (trend discovery, script writing, image/caption generation, timestamped video assembly). Emphasizes production readiness with extensive automated testing, Redis/Postgres/TimescaleDB state orchestration, and Prometheus/Grafana monitoring, reporting ~100x faster content production and improved engagement/viewership.”

Vivek Shrivastava

Screened

Director-level Technology Leader specializing in data platforms, AI, and media/AdTech transformation

Los Angeles, California24y exp

“Technology leader who built a unified platform for Fox live sports production operations starting in 2019, delivering an initial operational system on an ~18-month timeline while simultaneously scaling an in-house engineering team from a service-provider partnership. Led a security architecture for external vendors/partners using a separate Okta instance with zero-trust and passwordless authentication, and drove adoption through strong change management, documentation, and agile execution.”

Yash Nileshkumar Mirani

Screened

Mid-level Software Engineer specializing in AI agents, data pipelines, and cloud systems

Sunnyvale, CA5y exp

“Generalist software engineer with recent contract work at Vertex Pharmaceuticals shipping a desktop-integrated RAG assistant for lab scientists (2000+ pages ingested; ~40% support-ticket reduction in pilot). Previously owned Python/AWS financial automation services at Amazon operating at multi-billion-dollar scale, with strong strengths in API design, observability, and database/performance tuning; also built a React/TypeScript AI contract analysis product (ContractsGuy).”

“Entrepreneurial operator with 30+ years of experience building internal startup-style business cases inside private companies, securing capital/OpEx funding, and turning proposals into delivered technology initiatives. Brings a strong venture-informed mindset, including familiarity with Series A/B/C funding dynamics, combined with hands-on strength in business planning, financial modeling, team building, and execution.”

Utkarsh Mittal

Screened

Mid-level Software Engineer specializing in backend and distributed systems

Murray Hill, NJ5y exp

“Backend engineer who has owned large-scale systems from design through rollout, including a Dell rearchitecture that unified two regional platforms and delivered major latency gains while scaling the effort from 6 to 150 contributors. Also built an ESG analysis product in an ambiguous startup environment using AWS Lambda, Bedrock/Claude, Flask, and custom data pipelines, showing a blend of distributed systems depth and practical AI integration.”

Jahnavi Rao

Screened

Mid-level Full-Stack Developer specializing in FinTech and enterprise web applications

Overland Park, KS5y exp

“Backend engineer currently at Bank of America with experience in scalable backend services, microservices, and event-driven systems. Stands out for using AI pragmatically as a productivity accelerator while maintaining strong engineering judgment around reliability, security, and production validation, and shows strong architectural intuition for multi-agent workflows through a distributed-systems lens.”

Abhirup Chakraborty

Screened

Junior Software Engineer specializing in backend systems, AI, and search

Champaign, IL3y exp

“Built a complex graph-based search engine to find connections between people and has hands-on experience designing multi-agent coding pipelines that move features through implementation, test generation, testing, and sanity checks. Stands out for treating AI agents like an engineering team, with shared-memory coordination, queue signaling, and completeness-focused guardrails to improve reliability and reduce ambiguity.”