Vetted Amazon S3 Professionals

Mohammed Mudassir Hussain Shaikh

Screened

Entry-Level Full-Stack Software Engineer specializing in serverless AWS and AI applications

Tempe, AZ1y exp

“Built and deployed serverless AWS applications (Lambda/S3/RDS Proxy) including a NASA L’Space React + Python data analysis tool, focusing on performance for large datasets. Demonstrates strong cloud troubleshooting across compute and networking (CloudWatch-driven diagnosis, EC2 scaling, security group fixes) and a user-driven iteration loop that improved product usability with dynamic filtering and interactive UI.”

Purva Chakravarti

Screened

Senior Full-Stack Software Engineer specializing in scalable web apps, cloud, and blockchain/AI

Chino, California11y exp

“Full-stack engineer with strong production ownership across React/TypeScript, Node.js, and AWS (EC2/ECS/RDS/CloudWatch), including CI/CD, observability, and incident response. Delivered a secure RBAC workflow module end-to-end and achieved measurable gains (~30–40% latency reduction, ~50% error reduction) that lowered infra/ops costs. Comfortable in high-ambiguity startup environments—shipped a payment module within 2 days of joining with no documentation.”

“Full-stack product engineer who has built AI-assisted CRM and agent workflows in Project SARA and operational systems like payroll for a staffing platform. Stands out for combining React/TypeScript, Django/Postgres, real-time systems, and LLM orchestration with strong product instincts—delivering measurable gains in response time, conversion, and engineering leverage.”

“Analytics professional with hands-on experience turning messy Excel-based operational data into SQL/Python pipelines and Power BI dashboards, including a production bottleneck project that improved workflow efficiency by 20%. Also brings applied machine learning experience from a Databricks/PySpark loan risk scoring project using logistic regression and XGBoost on large-scale S3 data.”

“Analytics candidate with hands-on experience turning messy retail/customer data into clean reporting tables using SQL and PostgreSQL, then extending the work into Python-based reusable analysis workflows. They have applied segmentation, cohort analysis, and retention metric design to support dashboards and improve targeting, engagement, and repeat purchase performance.”

Srinivasan Gomadam Ramesh

Screened

Mid-level AI/Data Engineer specializing in agentic AI and data platforms

Redmond, WA7y exp

“AI/LLM engineer who built a production resume-parsing and candidate-matching platform at Quadrant Technologies, combining agentic LangChain workflows, VLM-based document template extraction (~85% accuracy), and a hybrid RAG backend for resume-to-JD search. Notably integrated automated LLM evals and metric-based CI/CD quality gates to catch silent prompt/model regressions, and led a 3-person team across frontend/backend/testing.”

Ruslan Labza

Screened

Mid-level Full-Stack Engineer specializing in modern web applications

San Francisco, CA6y exp

“Built and launched a production AI chat assistant inside a data processing platform, focused on helping users understand large table outputs and job results faster. Brings strong end-to-end product engineering across React/TypeScript frontend, backend APIs, and LLM integration, with a clear emphasis on reliability, safe behavior, and iterative quality improvements after launch.”

“Currently acts as a tech lead for a team of AI agents building a mobile application, with agents handling requirements, design, development, testing, documentation, and JIRA/Confluence updates. Stands out for combining multi-agent orchestration with strong human-in-the-loop review and a clear interest in AI governance and authorization controls.”

Pranav Gaikwad

Screened

Senior Software Engineer specializing in cloud-native platforms and supply chain systems

Atlanta, GA8y exp

“Backend and platform engineering leader with deep supply chain and warehouse systems experience, including building a company-wide MDM platform across five ERP systems and supporting a 72-microservice warehouse execution environment. Particularly compelling for AI-forward logistics roles: currently pursuing an AI-focused PhD, has published supply chain AI research, and holds a utility patent for AI-driven predictive analysis.”

“Built and shipped AI products at Humanitarians AI, including a full-stack multi-agent platform that consolidated six faculty AI tools into one interface and achieved 100+ user adoption, 70% less workflow switching, and a 6x latency improvement. Also designed a grounded document parser using FAISS and structured LLM outputs that reduced hallucinations by 60%, showing strong depth in both product-minded engineering and production AI systems.”

“Full-stack engineer with hands-on ownership of a production AI knowledge assistant used by 10,000+ daily users. Combines React/Next.js frontend work with FastAPI, AWS serverless, and RAG architecture using GPT-4, LangChain, and Pinecone, with measurable impact on relevance, latency, uptime, and support deflection.”

Saswata Deb

Screened

Mid Backend Engineer specializing in AI systems and LLM infrastructure

India4y exp

“Early-to-growth-stage B2B SaaS engineer from Sentisum who combined Python backend, data pipeline, and applied AI work with direct customer-facing product input. Particularly compelling for startup roles: they owned systems end-to-end, migrated transcription infrastructure to cut costs by ~93%, and built scalable async export and data-processing workflows over large enterprise conversation datasets.”

Alex Archer

Screened

Staff Full-Stack Engineer specializing in Python, AI systems, and cloud SaaS

Anna, TX8y exp

“Full-stack startup engineer from a 20-30 person company who led a legacy monolith breakup into microservices, improving response times by 30% and database performance by 20%. Has hands-on experience across React/Next.js, TypeScript, Go, Python, and AI/data pipeline work, including building AI-driven platforms for freight and publisher-focused B2B SaaS products.”

“Backend Python engineer who has built an LLM agentic tutoring/assignment helper with a custom pipeline for parsing visually complex textbooks (integrating AlibabaResearch VGT and implementing missing preprocessing from the paper), improving RAG grounding with ~90% cleaner extracted text. Also led major platform scaling work by refactoring monolithic image processing into Celery-based async microservices on AWS (GPU/CUDA + S3), and implemented Kafka streaming for payment webhooks with strict ordering, idempotency, and multi-zone fault tolerance.”

“Backend/data engineer with aerospace telemetry and reporting experience across RTX and other orgs, spanning Python/FastAPI microservices, AWS serverless/containers, and AWS Glue-to-Redshift analytics pipelines. Has led legacy modernization with parallel-run parity validation and incremental rollout, and demonstrates strong operational ownership (monitoring, incident response, and cost optimization).”

“Software engineer (Alpine Bank) focused on modernizing high-traffic customer-facing systems with React/TypeScript frontends and Spring Boot microservices. Has hands-on experience stabilizing and scaling event-driven architectures with Kafka (idempotent consumers, partitioning, retry queues) and building internal observability dashboards that materially sped up post-deployment verification and improved release confidence.”

“Software engineer with end-to-end ownership of a Qt/C++/QML desktop app for monitoring/configuring equipment, including hands-on UI performance optimization. Also built a web-based AI agent interface (React/TypeScript + Python Flask) with strong API contract discipline and async state handling, and improved microservices reliability using idempotency, DLQs, and observability. Created an internal CI/CD automation tool adopted across engineering and operations teams, adding safer rollbacks and better error messaging based on feedback.”

“Developed and productionized VL-Mate, a vision-language, LLM-powered assistant aimed at helping visually impaired users understand their surroundings and query internal knowledge. Emphasizes reliability and safety via confidence thresholds, uncertainty-aware fallbacks, hallucination grounding checks, and rigorous offline + user-in-the-loop evaluation, with experience orchestrating multi-step LLM pipelines (LangChain-style and custom Python async) and deploying on containerized infrastructure.”

“Full-stack intern at Rebel who owned backend work on a cross-platform music platform using Python/Django with MongoDB, implementing user-focused REST APIs end-to-end. Also built CI/CD pipelines (Jenkins/GitHub Actions) to containerize and deploy to AWS, and has experience integrating Kafka-based real-time event processing with reliability and observability practices.”

“Robotics/ML engineer who benchmarks and adapts open-source robot action models, building synthetic datasets in Isaac Sim and modifying vendor code to scale training across multiple GPUs. Also built a production-style computer vision pipeline at Zortag—training a tiny YOLO-based classifier for fake-vs-real label detection and deploying it in a real-time iOS app with additional display/spoof detection.”

“Built SparkyAI, a gamified college-essay writing assistant (hackathon project at ASU in 2025) using React/styled-components, Firebase (OAuth/DB), and OpenAI APIs, with concrete scalability and performance measures like rate limiting, indexed queries, code splitting, and conversation caching. Also designed a global low-latency voice-to-LLM architecture leveraging WebRTC, regional containerized services, global load balancing, streaming STT/TTS, and end-to-end encryption with minimal logging.”

“Built a production lead-generation system using AI agents that researches the internet for relevant leads and integrates RAG-based contact enrichment/shortlisting aligned to existing CRM data, enabling sales reps to focus more on selling. Also has hands-on AWS data orchestration experience (Glue, Step Functions) moving raw data into Redshift and evaluates agent performance with human-in-the-loop plus BLEU/perplexity metrics.”

“CloudData engineer who productionized an LLM assistant for a warehouse/logistics customer by wrapping it as a versioned, containerized API with guardrails, deterministic post-processing, and full observability. Experienced diagnosing real-time RAG/agentic incidents (latency spikes and confident-wrong answers) using trace-based isolation, replay in staging, retrieval tuning, and canary releases. Regularly runs technical demos/workshops and partners with sales on security/IAM, SLAs, and pilot rollouts to drive adoption.”