Vetted Amazon EC2 Professionals

“Full-stack developer with strong cloud/on-prem focus (AWS, VPC networking) who has improved production reliability by bringing manually created IAM/security group resources under Terraform and standardizing environments. Demonstrated end-to-end troubleshooting across app + infrastructure + networking (traffic capture revealed proxy response truncation) and delivered Python-based monitoring/reporting enhancements that improved ops visibility and turnaround.”

“Built and shipped a production agentic LLM analytics platform that lets non-SQL business users query relational databases in plain English via a RAG + LangChain/LangGraph workflow and FastAPI service. Emphasizes safety and reliability with guardrails (validation/access control), testing/evaluation frameworks, and performance optimization (caching, monitoring, Dockerized scalable deployment), reducing dependency on data teams and speeding analytics turnaround.”

“Software/ML engineer who built a production automated job-finding and cold-email personalization system for Fortune 500 outreach, using JobSpy for dynamic scraping, LangChain orchestration, and LLM+vector DB semantic search with grounding/relevance metrics and guardrails. Also delivered a predictive investment analytics platform for financial advisors, communicating results via Tableau dashboards and portfolio KPIs like Sharpe ratio and drawdowns.”

“Product-focused full-stack engineer (70% application-layer) who has shipped multi-tenant RBAC for a formerly single-tenant platform, cutting infrastructure costs by 50%. Built high-impact customer-facing features including analytics dashboards (40% retention lift) and a React/TypeScript scheduling grid that reduced navigation time by 60% and setup time by 80%, with solid AWS operations and Postgres performance tuning experience.”

“Built an Arduino-based obstacle-avoiding robot using sonar/laser sensors and improved performance from 0.60 to 0.87 accuracy through sensor-fusion thresholding and iterative tuning. In an internship, optimized a legal-document NLP pipeline by switching to a distilled/quantized transformer and offloading inference to a GPU-backed Flask service, cutting inference time by 40%+ without added infrastructure spend.”

“Full-stack engineer with strong ownership in fast-moving environments: designed and shipped a pre-order/campaign inventory system (NestJS + Strapi + Datadog) that freed 34% warehouse space and reduced stock risk to ~5.7%. Also built rapid, high-impact logistics features (Spot Sales) that drove last-mile cost to ~0 in ~40 days, and has hands-on AWS/Terraform/CI-CD experience including deploying a global RAG system with Pinecone, Datadog, and PagerDuty.”

“QA automation engineer with deep experience owning end-to-end Cypress/JavaScript test suites (smoke, regression, and API contract tests) integrated into GitHub CI with merge gating and rich reporting. Demonstrated healthcare enrollment domain expertise by catching a critical eligibility versioning/overwrite defect via API + DB assertions that UI tests missed, then hardening the pipeline with contract tests and idempotency checks.”

“Has worked at a startup within an incubator program and is a certified mentor with a startup mentorship non-profit focused on early-stage/pre-funded companies. Not currently seeking to found a company or take equity-only roles, but open to working at very early-stage companies (pre-launch/pre-revenue/pre-funded).”

“Full-stack engineer in healthcare and enterprise analytics who has shipped event-driven, near-real-time systems (Spring Boot microservices + Kafka + AWS) and large-scale patient/provider portals (50k+ users). Strong in production reliability and performance—measurably reduced claims latency (27%), cut support tickets (25%), and handled real AWS scaling incidents end-to-end. Also built a Python REST control plane for SDN routing integrated with external reinforcement learning agents.”

“Built and shipped a production RAG assistant using GPT-4, LangChain, and Pinecone/FAISS to search 50K+ institutional documents, with a strong focus on groundedness and hallucination reduction through retrieval optimization and re-ranking. Pairs this with a metrics-driven evaluation/monitoring approach (BLEU/ROUGE, manual sampling, logging) and workflow automation via Airflow, and has experience translating stakeholder needs into iterative AI prototypes.”

“Backend/platform engineer with State Farm experience modernizing and scaling an enterprise consolidated payment data platform and event-driven pipelines. Built cloud-native payment architecture (ECS->EKS) handling millions of financial transactions/day and high-volume telemetry (~100M events/day), with strong schema governance (Avro + schema registry) and production operations/incident mitigation driven by observability.”

“Built and shipped a production LLM-powered customer support agent for an EV charging platform using RAG plus internal APIs, automating session/payment issues and ticket routing. Emphasizes production readiness via guardrails, schema validation, state-machine orchestration, monitoring, and continuous evals, delivering a reported 35–40% reduction in support tickets and improved customer satisfaction.”

Dhairya Desai

Screened

Senior AI/ML Engineer specializing in healthcare NLP and predictive analytics

Chicago, IL13y exp

“ML/NLP engineer with healthcare and industrial IoT experience: built an Optum pipeline that converted 2M+ physician notes into structured entities and linked them with claims/pharmacy data to create an actionable patient timeline. Deep hands-on expertise in production NER, entity resolution, and hybrid search (Elasticsearch + embeddings/FAISS), plus robust data engineering practices (Airflow, Spark, data contracts, auditability) and experimentation-to-production rollout via shadow mode and feature flags.”

Piyush Rajendra

Screened

Mid-level AI/ML Engineer specializing in production RAG systems and MLOps

Athens, GA4y exp

“Built and deployed a GPT-4 + Pinecone RAG system that lets users query large internal document collections with grounded, cited answers. Demonstrates strong applied LLM engineering (chunking experiments, hallucination controls, metadata recency boosting) plus production-minded evaluation/monitoring and performance tuning (rate-limit mitigation via pooling/batching). Also effective at translating complex AI concepts to non-technical stakeholders through prototypes and live demos, helping secure client sponsorship.”

Daanesh Potnuri

Screened

Mid-Level Full-Stack Engineer specializing in API-driven microservices and cloud delivery

5y exp

“Software engineer with hands-on experience building a decentralized file-sharing dApp, bridging a React frontend with Ethereum smart contracts via Web3.js and integrating IPFS for decentralized storage. Demonstrates a rigorous, measurement-driven approach to performance optimization (profiling + benchmark/regression loop) and strong ownership in high-stakes environments, including Fircosoft sanctions platform optimization and rapid production hotfixes for user-impacting issues.”

Hrishi Kamal Earla

Screened

Senior Full-Stack Java Developer specializing in cloud-native FinTech microservices

Remote, CA7y exp

“JavaScript/React engineer with hands-on open-source library contribution experience, including thoughtful PRs that improved error handling, API flexibility, and added features backed by tests and documentation. Demonstrates a profiling-first approach to UI/runtime performance (memoization, component splitting, render-path optimization) and strong community support skills—reproducing edge cases, delivering sustainable fixes, and communicating workarounds and releases.”

Varun Kothapalli

Screened

Mid-level AI/Machine Learning Engineer specializing in Generative AI, NLP, and MLOps

Saint Louis, MO6y exp

“Built a production LLM/RAG document analysis system for large financial documents (credit reports/PDFs) to help business analysts extract insights faster. Implemented end-to-end pipeline orchestration with LangChain, vector search (e.g., FAISS), and hallucination controls (context grounding, similarity thresholds, and no-answer fallback), delivered as a Dockerized Python API.”

Sreedivya Nagalli

Screened

Junior AI/ML Engineer specializing in deep learning and full-stack ML applications

2y exp

“Built and operated a production-used RAG-based AI study planner (GPT-4 + FAISS) that handled 250+ concurrent users, with real-world reliability engineering (caching, fallbacks, schema validation, Redis state, monitoring). Also has healthcare data integration experience at Medinet Analytics, standardizing messy EHR/practice-management data with canonical schemas, idempotency hashing, and compliance-grade audit trails.”

Navya Sureddy

Screened

Mid-level Full-Stack Developer specializing in Angular/React and Spring Boot

Columbus, IN5y exp

“Full-stack engineer with experience at Cummins owning production features end-to-end (React/TypeScript + Node + Postgres) and operating them in AWS (EC2/RDS/S3/IAM) with CloudWatch-based observability. Also built resilient ETL and third-party integrations, including an AWS Glue–S3–Redshift pipeline hardened with validation, idempotent UPSERTs, retries/backfills, and quarantine handling to prevent bad or duplicate data.”

Darshan Togadiya

Screened

Mid-level Full-Stack Engineer specializing in cloud-native web apps

Rancho Cucamonga, CA5y exp

“Full-stack engineer in an early-stage startup who built an EV charger monitoring and payments dashboard from scratch, owning UI/UX (Figma), React frontend, Node/Postgres APIs, and production deployment/ops (Firebase + AWS). Demonstrated measurable impact (40% fewer reconciliation errors) and strong reliability chops through multi-source energy/payment ingestion, idempotent pipelines, and CloudWatch-driven incident resolution.”

Manali Patil

Screened

Senior Software Engineer & Engineering Manager specializing in cloud backend and manufacturing MES

Santa Clara, CA9y exp

“Customer-facing engineer who led recurring midnight ERP data-feed/B2B integrations from prototype to production, building reusable APIs and using Hangfire for job scheduling. Known for tight weekly customer iteration, strong documentation and test coverage (80%+), and cross-functional problem-solving with Operations/Quality/NPI to resolve data-collection and manufacturing-process constraints; has 2 customers live on the integration.”

Sravya Chunduri

Screened

Mid-level AI/ML Engineer specializing in LLM, NLP, and MLOps

Virginia, USA4y exp

“AI/ML Engineer with 3+ years of experience spanning RAG pipelines, MLOps, large-scale data workflow automation, and resilient Playwright-based UI automation. At Black Hawk Network and Wipro, they describe shipping production systems with strong observability and compliance controls, including reducing flaky automation failures from 30% to under 2% and automating 3+ TB/day reconciliation workflows.”

“Full-stack product engineer with hands-on experience shipping React/TypeScript applications on AWS serverless infrastructure with Postgres. Stands out for combining measurable performance optimization (~30% faster APIs), UX improvements that lifted activation by 25%, and pragmatic platform thinking through reusable hooks and safe multi-tenant dashboard customization.”

“Backend engineer with hands-on ownership of a production POS platform, including architecture, CI/CD, Kubernetes deployment, and live incident handling. Also built a RAG-based document Q&A system using OpenAI/Anthropic with hybrid retrieval, evaluation metrics, and fallback logic, showing both traditional backend depth and practical applied AI experience.”