Vetted Microservices Professionals

Jonathan McGrath

Screened ReferencesModerate rec.

Senior Full-Stack/AI Engineer specializing in mobile and web product development

San Francisco, CA14y exp

“Built an end-to-end mobile + web Q&A marketplace connecting users with professionals, including real-time chat and Stripe-based monetization (products/subscriptions). Hands-on with scaling Firebase/Firestore (subcollections, composite indexing, pagination) and mobile caching/sync challenges. Also created an internal AI-driven report generator that turned chatbot outputs into curated graphs and PDFs for marketing, iterating based on stakeholder feedback.”

Dale Durham

Screened References

Principal Software Engineer specializing in SaaS architecture and cloud platforms

Hammond, IN23y exp

“Veteran B2B SaaS engineer/technical leader with 13+ years in production systems across medical, legal, and academic conference markets. Stands out for combining hands-on full-stack and microservices architecture work with measurable business impact, including leading an early startup internationalization effort that more than doubled revenue in under a year and modernizing a declining conference platform into the market leader in its niche.”

Punith Jayaramu

Screened

Mid-Level Full-Stack Software Engineer specializing in FinTech and Mortgage systems

Auburn Hills, MI3y exp

“Full-stack engineer with deep AWS serverless and reliability experience in fintech/underwriting systems, including eligibility scoring and dynamic rule deployments. Built and productionized an LLM-powered incident RCA assistant (Bedrock Claude 3 + custom RAG + React) achieving 92% precision and ~75% MTTR reduction, with mature guardrails (evals, drift monitoring, HITL, audit logs) and strong operational rigor (canaries, chaos testing, DLQ remediation).”

Sridhar Duggireddy

Screened

Executive Technology Leader specializing in Cloud Platforms, DevOps, and AI/ML

Tampa, FL21y exp

“Early-stage startup CTO helping build an AI-powered parenting assistant app with features spanning advice, shopping, task management, and inventory management. The team is currently testing an MVP with their network while the candidate simultaneously learns the seed/Series fundraising process through Connectd and early investor conversations.”

Drashti Magia

Screened

Junior AI Engineer specializing in LLMs, multimodal ML, and applied machine learning

San Jose, CA3y exp

“Software engineer with a disciplined, production-minded approach to AI-driven development: uses ChatGPT, Claude, GitHub Copilot, and scoped coding agents to accelerate delivery without giving up architectural judgment. Notably applied a multi-agent workflow on ClinicOps Copilot, using agents for planning, Bedrock/RAG scaffolding, and failure testing while personally owning architecture, grounding quality, and end-to-end review.”

“Frontend-leaning engineer at Exchange Lodge who recently took over an entire system end-to-end after team attrition, expanding into backend and platform ownership. They modernized a JSON-driven UI platform into a TypeScript/Angular microfrontend architecture, reporting an ~85% productivity gain, and also rebuilt documentation and developer workflows into a monorepo with AI-friendly Markdown and Mermaid diagrams.”

“Engineering leader who delivered a Magento-to-Shopify rebuild ahead of schedule while maintaining 100% team retention over 4 years at Hydrobuilder Holdings. Also built and ran a concert-listing platform (TourVolume) in production for 12+ years (Webby Honorable Mention) and later open-sourced the full codebase. Deep experience in auth/API platforms on AWS, including moving critical auth services from Lambda to ECS to eliminate cold-start latency.”

Mark Wlodawski

Screened

Senior Unity Developer specializing in AI/LLM systems and multiplayer VR

Orlando, FL11y exp

“Backend/data engineer focused on AWS-native Python systems: built a FastAPI microservice on ECS/Fargate serving real-time analytics at millions of daily requests with strong reliability (OAuth2/JWT, retries/timeouts, correlation IDs) and autoscaling. Also delivered Glue/PySpark ETL pipelines to curated S3 Parquet/Athena with schema evolution + data quality controls, owned Airflow pipeline incidents, and has a track record of measurable performance and cost optimizations (e.g., ~80%+ query latency reduction; reduced logging/NAT/Fargate spend).”

“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”

Susheel Thimlapur

Screened

Mid-level Full-Stack Engineer specializing in web applications and cloud systems

Bloomington, IN4y exp

“Software developer at Indiana University who built a phishing training platform using TypeScript/React, Node, GraphQL, and DynamoDB, including an admin tool to monitor student completion and send automated reminder emails. Also created TechRent, an IU-only electronics rental marketplace concept focused on improving security by restricting access to university students.”

Ruchi Desai

Screened

Senior Backend Engineer specializing in cloud-native .NET microservices

Frisco, TX8y exp

“Healthcare-focused full-stack engineer who has spent their career in small startup-like teams, owning products end-to-end from business requirements to architecture, implementation, deployment, and support. They’ve built critical hospital-facing systems including patient intake, insurance eligibility, appointment search at scale, and configurable multi-tenant admin platforms using React, Node.js, .NET, AWS, MySQL, and Elasticsearch.”

“New grad focused on AI systems and agent-based development, with hands-on experience using LLMs as a coding partner and building RAG-based document processing workflows. Stands out for practical experimentation with semantic chunking, retrieval optimization, and multi-agent architectures, including redesigning a RAG workflow by adding a reasoning agent to improve response accuracy and reliability.”

“Candidate has hands-on experience at Cognizant building production-grade automation and integration solutions across Python ML services, Java microservices, Kafka, and Selenium-based UI testing. They stand out for a strong reliability mindset—covering failure modes, observability, flaky test hardening, and translating ambiguous payment-system business processes into resilient end-to-end automated workflows.”

“Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.”