Vetted Amazon EKS Professionals

“Built and productionized an LLM-enhanced version of the WeDAA platform to auto-generate microservice architecture diagrams and support code generation from user prompts, including a practical solution for non-overlapping canvas object placement via coordinate templates. Experienced in diagnosing agentic workflow failures using AWS Strands agents with feature-flagged debug logging, and frequently supports sales through tailored demos and POCs to drive adoption.”

“Built a production-grade, semi-automated document recognition and classification system for large volumes of scanned PDFs, starting from little/no labeled data and handling highly variable scan quality. Deployed on AWS using SageMaker + Docker and orchestrated on EKS with a microservices design that scales CPU-heavy OCR separately from GPU inference, with strong reliability controls (validation, fallbacks, retries, readiness probes).”

“Full-stack/product-leaning engineer in logistics and high-traffic portals who ships production AI features: built an AI-assisted shipment status Q&A system using Pinecone + GPT-4 and a high-volume Python ingestion pipeline (500K+ records/day), delivering 35% fewer support tickets and cutting resolution time from 11 to 4 minutes. Also led a legacy Angular-to-React/TypeScript rebuild that boosted Lighthouse performance from 60 to 90, and has hands-on AWS EKS operations experience including resolving a 3x traffic scaling incident.”

“Backend/AI engineer who built production-grade intelligence systems in high-stakes domains including tax/legal document analysis and brain tumor MRI workflows. Stands out for combining LLM/RAG product delivery with strong engineering rigor around retrieval evaluation, grounding, validation, observability, and safe fallbacks—turning impressive demos into systems users could actually trust.”

“Full-stack engineer with hands-on ownership of a production cart-audit workflow redesign, spanning schema redesign, Rails APIs, and frontend navigation for non-technical operations users. Particularly strong in modernizing legacy systems safely with rollback paths and clearer data relationships, while also building AI-powered side projects using Flask, LangChain, and GPT for GitHub code analysis.”

Manas Agarwal

Screened

Junior Full-Stack Software Engineer specializing in Python APIs, React, and cloud AI integrations

Superior, CO2y exp

“Customer-facing software engineer who builds and deploys practical AI/RAG solutions (e.g., an AI assistant for searching billing PDFs) by deeply understanding support workflows and iterating with users. Demonstrates strong production instincts—quickly stabilizing peak-traffic API timeouts with caching/background jobs, then implementing durable fixes with proper monitoring and maintainable code practices.”

Ofek Shaltiel

Screened

Mid-Level Full-Stack Software Engineer specializing in AI-enabled web platforms

Dallas, TX4y exp

“Backend/AI engineer in construction tech (HyperWater AI) who delivered major production performance wins (analytics API from ~1 hour to 0.5s) and shipped LLM features for parsing subcontractor manifests into CSI divisions with human-in-the-loop review. Also built a freelance agentic document-verification system using OCR + RAG over pgvector with robust retry/escalation logic and user feedback loops.”

Sanskar Pandey

Screened

Junior Full-Stack Java Developer specializing in FinTech microservices

OH, USA3y exp

“Full-stack engineer with production experience building a real-time order tracking system using React + Firebase/Firestore, emphasizing audit-friendly data modeling, state-machine-based status transitions, and strong post-launch ownership (performance, security rules, reliability). Demonstrated measurable frontend performance gains by isolating real-time updates to dynamic components and applying memoization, plus backend reliability patterns (idempotency, retries) and SQL query/index optimization validated with EXPLAIN ANALYZE.”

“Candidate is actively using AI-assisted development tools, including MCP server integrations with Copilot, to generate boilerplate test scripts, validate code standards, and handle package updates. They also have hands-on experience choosing different agents based on task requirements and serving as an admin for AI tool access.”

“Software engineer with a strong builder mindset who has worked across ML, backend, and frontend systems. Notably built an AI-driven predictive autoscaler for Kubernetes from scratch using Prometheus, TensorFlow, Flask, and Spring Boot, and also delivered customer-facing automation features in financial document processing by working directly with auditors to translate domain rules into product logic.”

SAUD SALEEM

Screened

Senior Python Engineer specializing in scalable backend and distributed systems

Stockton, CA6y exp

“Startup-oriented full-stack engineer who has worked across distributed systems, data, and compliance-heavy B2B SaaS products. They led an incremental migration from a monolith to an event-driven microservices architecture and also built high-volume healthcare ETL pipelines, giving them a strong mix of product engineering, platform modernization, and data-intensive backend experience.”

“Built and deployed a production RAG-based multilingual analytics assistant for healthcare operations, enabling non-technical teams to query claims/EHR and risk metrics with grounded explanations. Demonstrates strong end-to-end LLM system engineering (retrieval tuning, re-ranking, hallucination controls, verification layers) plus workflow orchestration (Airflow/Composer/Step Functions) and stakeholder-driven iteration via prototypes and dashboards.”

“Software/product engineer who owns customer-facing internal platforms end-to-end, with deep experience building data pipeline health and data quality tooling (near-real-time alerting and ops dashboards). Strong in React/TypeScript + Python REST architectures and microservices with RabbitMQ, emphasizing reliability patterns (idempotency, DLQs, correlation IDs) and fast, safe iteration via feature flags, testing, and observability.”

“Built and deployed a production generative-AI copilot at Tungsten that automates invoice/form extraction template creation, reducing weeks of manual model-building work. Combines fine-tuned LLMs (PyTorch/HuggingFace) with OpenCV layout grounding to reduce hallucinations, and runs an end-to-end Kubeflow-based MLOps pipeline with drift monitoring, canary releases, and automated retraining.”

“Cloud/DevOps-focused engineer with hands-on experience building Azure DevOps CI/CD pipelines for containerized applications deployed to AKS, including security scanning, approvals, versioned artifacts, and rollback. Also implemented Terraform-based IaC for Azure (VNets/subnets/NSGs/AKS) with modular design, remote state/locking, and drift detection; resolved a real deployment outage caused by an Azure RBAC permission change.”

“LLM engineer who has deployed a production RAG system (LangChain/FAISS/FastAPI) for enterprise semantic search, tackling real-world latency by LoRA/PEFT fine-tuning and grounding outputs with retrieval. Brings strong MLOps (Docker, AWS EKS, CI/CD, MLflow) plus stakeholder-facing explainability experience using SHAP to align ML-driven financial guidance with non-technical domain experts.”

“Full-stack/backend-heavy engineer with strong healthcare domain expertise, focused on modernizing regulated platforms for payroll, billing, and caregiver operations. Notably led a legacy Express.js billing platform migration to modular NestJS services that enabled same-day deployments and unblocked enterprise customer onboarding, while bringing deep experience in compliance, auditability, and production reliability.”

“Frontend-focused engineer with experience in healthcare and fintech dashboards, especially around real-time data delivery and performance. In a growth-stage startup environment, improved dashboard architecture by replacing polling with React Query and GraphQL, leading to more responsive and reliable user experiences and fewer stale-data complaints.”

“Backend-focused engineer with deep healthcare interoperability experience, building Go-based distributed systems for live clinical data exchange across hospital systems. Stands out for combining startup-style ownership with HIPAA/compliance rigor, including creating a reusable Go microservice template and improving reliability across Kafka, PostgreSQL, and Kubernetes-based production platforms.”

“Full-stack engineer with deep startup experience building products from scratch under ambiguous requirements. Delivered a scalable, admin-configurable notification platform (Spring Boot/Java/Kafka) supporting 50+ notification types across 3 channels for 10k+ users, cutting new notification setup to ~5 minutes. Also built a Tinder-meets-LinkedIn job-swiping app (React/TS + Node/Prisma) and has hands-on AWS production ops (ECS/EKS, RDS, CloudWatch) plus multiple third-party integrations (Stripe, QuickBooks, Twilio).”

“Senior backend/data engineer who has built and maintained HIPAA-compliant, real-time clinical FastAPI services on AWS, orchestrating ML/LLM and vector DB calls with strong reliability patterns (auth, timeouts/retries, graceful degradation, idempotency). Also delivered AWS IaC/CI-CD (Terraform/Helm/GitHub Actions) across EKS/Lambda/SageMaker and built Glue/Spark ETL with schema evolution and data quality controls, plus demonstrated large SQL performance wins (15 min to <9 sec) and hands-on incident ownership.”

“Owned and stabilized Decathlon e-commerce payment services, taking a prototype reliability effort to production by implementing failure detection/retries, load testing, and DB performance optimizations—reducing payment failures and cart abandonment. Also demonstrates an LLM/agentic workflow support mindset with strong observability, rapid incident diagnosis, and durable prevention via RCA, safeguards, and regression/replay testing, plus experience supporting sales/support with technical reassurance.”

“Built and deployed a production AI accessibility platform that turns chart and image-based graphs into real-time audio narratives for visually impaired users. Implemented a ResNet-based CV + OCR + NLP + TTS pipeline and improved performance through preprocessing, Redis caching, and Kubernetes autoscaling/rolling updates on AWS to handle traffic spikes with no downtime.”

“Full-stack engineer with strong production experience improving performance and reliability of data-heavy analytics products. Has shipped end-to-end features spanning Node/Express + PostgreSQL + Redis and React/TypeScript, deployed via Docker/GitHub Actions to AWS EKS with Helm, and monitored with Datadog/CloudWatch; also built a Python compliance automation backend for AWS security monitoring with RBAC, versioned REST APIs, and resilient throttling-aware processing.”