Vetted GitHub Professionals

SreeHarsha Kelam

Screened

Senior DevOps / Site Reliability Engineer specializing in cloud infrastructure

Atlanta, GA5y exp

“GCP-focused database/platform engineer with hands-on production experience operating Cloud SQL at scale, especially around performance tuning, reliability, and day-2 operations. Has supported migration from a single-instance database setup to a more scalable GCP architecture and built Terraform/Python automation for provisioning and recovery workflows.”

“Frontend-focused software developer with experience building analytics software and web-based process control systems. Has worked with TypeScript, MVC architecture, Redux, and Redux Toolkit, including handling state management challenges in a real-time analytics application.”

“Candidate has hands-on project experience in healthcare analytics, using SQL, Python, and Power BI to analyze CMS hospital readmissions and HRRP penalty risk in Florida. Their work centers on turning messy CMS flat files into reporting-ready datasets, benchmarking hospitals against national references, and surfacing financial risk through dashboards.”

Tara Worrell

Screened

Senior Software Engineer specializing in backend systems, AI/LLM integration, and cloud infrastructure

Brooklyn, NY6y exp

“Backend engineer with experience in highly regulated and high-stakes systems, including an airline crew messaging platform requiring near-zero-error real-time operations and a HIPAA-compliant mental health application built from an early-stage concept. They also show strong operational maturity, having owned a GoDaddy production incident through resolution and then led deployment pipeline improvements that reduced build failures by 40% and doubled deployment frequency.”

“Senior data scientist with ~5 years’ experience building production ML/NLP systems in finance (Wells Fargo) and deep learning for sensor analytics in connected vehicles (Medtronic). Has delivered end-to-end platforms combining time-series forecasting with transformer-based NLP, including automated drift monitoring/retraining (MLflow + Airflow) and standardized Docker/CI/CD deployments; achieved a reported 22% precision improvement after domain fine-tuning.”

Adhiraj Chadha

Screened

Junior Software Engineer specializing in AI, data, and full-stack applications

Amherst, MA3y exp

“Builder with a mix of backend engineering, product instinct, and startup execution: they shipped a legal BI platform from scratch that handled 1,000+ cases, cut reporting time 80%, and saved $30K annually. They also move quickly in ambiguous environments, from launching a roommate app across iOS/Android after user discovery to building a RAG system with a 50+ case evaluation suite and a cloud dev environment in under 48 hours.”

“Front-end engineer with deep expertise in sophisticated browser-based UIs for manufacturing ERP workflows, including a custom canvas-rendered scheduling timeline used by production planners all day. Stands out for combining low-level browser performance knowledge with close observation of expert users, resulting in a tool that replaced long-standing spreadsheet workarounds and significantly improved scheduling efficiency.”

Manoj Manjunatha

Screened

Mid-level Software Engineer specializing in full-stack systems and applied AI

Remote, USA4y exp

“Built the backend for “codeGuard,” an AI-powered static code analysis platform, using FastAPI and Docker. Structured the system into API/service/execution layers and addressed heavy-workload container resource/cleanup issues via strict CPU/memory limits and a queued execution model.”

“Front-end developer with experience spanning agency, product, and brand-driven web work, including Just Eat and SEO-focused client projects. Particularly strong in building multi-brand, config-driven booking flows, improving conversion with analytics, and modernizing legacy sites with cleaner CMS and TypeScript/React-based approaches.”

“Full-stack Python engineer who has owned internal automation products from requirements through production, including a financial reporting platform that improved deployment time by 45% and raised reporting efficiency to 98%. Also built an AI-powered movie recommendation engine using collaborative and content-based filtering, with hands-on experience across frontend, backend, data pipelines, and ML evaluation.”

“Full-stack engineer with production experience across React/TypeScript, Node/Express, and Java/Spring Boot, operating containerized systems on AWS (EKS/ECS/EC2/RDS/S3) with strong observability (CloudWatch/Grafana). Notable for fixing a real checkout/order-placement failure end-to-end by adding frontend submission guards and backend idempotency with Redis + Kafka deduplication, then validating impact via technical metrics and business KPIs. Has also built Kafka-based integrations/pipelines with robust retry/backfill/reconciliation patterns in retail and banking contexts.”

“Built a healthcare app backend and supporting product pieces from scratch for Maverick Health—covering database schema, API structure, Node.js implementation, and UI design in Figma—while targeting 10,000 patients and keeping AWS run costs to ~$20–$30/month. Shipped an Android closed beta on Google Play and handled real-world launch hurdles like privacy policy compliance and push notification infrastructure.”

“Backend/integration-focused engineer in the Maryland area with production experience building FastAPI REST services secured with OAuth2.1/JWT and reliability patterns (timeouts, selective retries, idempotency, centralized error handling). Has delivered AWS-integrated MuleSoft/CloudHub solutions and supported AWS Glue ETL workflows, plus demonstrated strong SQL tuning with a 30–40s to 3–5s performance improvement.”

“Software engineer/product owner who has led end-to-end delivery of AI and content-management platforms, including building RAG-based reliability improvements and migrating fragile systems to containerized AWS ECS/Kubernetes with Terraform-managed CI/CD. Experienced designing event-driven microservices (SQS/SNS/RabbitMQ), scaling queue consumers with autoscaling, and creating internal Python tooling to standardize data connectors (e.g., BigQuery/Airtable/internal APIs) to speed iteration.”

“Accenture engineer who owned an insurance e-application end-to-end and drove incremental releases that reduced recurring production issues. Also built a TypeScript/React (Next.js) + NestJS microservices platform using PostgreSQL, Redis, Stripe, and Kafka, with strong focus on decoupling, eventual consistency, and scaling consumers under load. Created a hackathon chat-based internal assistant that used live form context and documentation-grounded answers to help agents resolve customer queries during form filling.”

“Built and productionized an enterprise retrieval-augmented generation platform for internal knowledge over large unstructured corpora, emphasizing trust via strict citation/grounding and hybrid retrieval (BM25 + FAISS + cross-encoder re-ranking). Demonstrates strong scaling and cost/latency optimization through incremental indexing/embedding and index partitioning, plus disciplined evaluation/observability practices. Has experience operationalizing pipelines with Airflow/Databricks/GitHub Actions and partnering closely with risk & compliance stakeholders on auditability requirements.”

“Built and deployed a production SecureAIChatBot (RAG-based) for secure internal information retrieval, using embeddings/vector search, GPT models, monitoring, and safety filters. Focused on real-world production challenges like latency and output consistency, applying caching, retrieval scoping, smaller models, and controlled prompting, and used LangChain to orchestrate the end-to-end workflow.”

“Software engineer experienced in e-commerce systems, building customer-facing features and internal operations tools with TypeScript/React frontends and Spring Boot microservices. Demonstrated measurable performance wins (order-tracking API improved from ~2s to <700ms) and strong event-driven reliability practices with RabbitMQ (idempotency, DLQs, retry/backoff), including resolving a production queue backlog incident. Built an ops dashboard with real-time cross-service order tracing that became a daily tool for support/ops and reduced escalations to engineering.”

“Backend/AI engineer who built an AI icon-generation SaaS backend (Java/Spring Boot, MongoDB) on AWS, including async job processing with idempotency and S3-based result storage to handle traffic spikes. Also shipped applied AI in finance—an end-to-end fraud detection pipeline with risk scoring—and designed a banking support AI agent with strict guardrails, audit logs, and human-in-the-loop escalation.”

“Growth/creative marketer from Esleydunn Games who uses Google Analytics to integrate cross-channel performance data (TikTok, YouTube, LinkedIn, Facebook) and run structured A/B tests on video ad length and layout. Reported reducing CPA by 20 per customer when leveraging YouTube and TikTok, and improved CTR through CTA/button placement testing and ongoing user-feedback loops (forum/WeChat topics).”

“JavaScript/Node.js engineer who contributes to open-source utilities focused on API integrations and JSON validation, including a 30–35% throughput improvement by profiling and optimizing deep-clone-heavy code paths. Strong in performance tooling (Node performance hooks, Chrome DevTools flame graphs), incremental/test-driven changes, and community-facing issue triage plus developer-friendly documentation.”

“Full-stack engineer with recent Mutual of Omaha experience building a cloud-native microservices application in Java/Spring Boot with a React/Angular frontend, integrating multiple AWS services (Lambda, S3, DynamoDB, SQS). Has hands-on experience operationalizing AI features via OpenAI/AWS Bedrock and improving reliability/performance through caching, async processing, and CI/CD pipeline optimization.”

“AI/LLM engineer with production experience in legal tech: built a GPT-4 + LangChain RAG summarization system at Govpanel that reduced legal case-file review time by 50%+. Previously at LexisNexis, orchestrated end-to-end Airflow data/AI pipelines processing 5M+ legal documents daily, improving ETL runtime by 35% with robust validation, monitoring, and SLAs.”