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Vetted Amazon EKS Professionals
Pre-screened and vetted.
SaiDheerajReddy Gadikota
Mid-level AI Engineer specializing in agentic systems and enterprise LLM platforms
“Current AI engineer at a startup who has spent the last year architecting multi-agent systems for software development workflows. Stands out for combining LLM speed with engineering discipline—using tools like Pydantic, LangGraph, and LangChain to build reliable, production-ready agent workflows with validation, routing, and retry logic.”
Dhairya Gajjar
Mid-Level Software Engineer specializing in Healthcare Data Platforms
“Backend/ML engineer with healthcare domain experience building secure Medicare/Medicaid data APIs and real-time patient risk scoring. Shipped an end-to-end ML pipeline (scikit-learn/XGBoost) served via SageMaker and integrated into Flask APIs, with strong production reliability practices (Kafka schema validation, regression replay, observability, drift monitoring, and human-in-the-loop guardrails).”
Pavithran Gnanasekaran
Entry-level Software Engineer specializing in AI/ML and full-stack systems
“Internship-built full-stack systems spanning HR employee-record portals and internal data-quality dashboards (Flask + SQL + React), emphasizing data integrity and rapid MVP iteration. Also implemented Flask microservices with RabbitMQ for distributed task processing, addressing duplication/ordering issues with idempotency, durable queues, and correlation-ID logging; delivered quantified productivity gains for HR teams.”
Senior Full-Stack Software Engineer specializing in Python microservices and cloud platforms
Mid-Level Full-Stack Software Engineer specializing in cloud-native security & compliance platforms
Mid-level Full-Stack Developer specializing in AI-powered analytics platforms
Mid-level Software Engineer specializing in Java/Spring microservices on AWS
Mid-Level Full-Stack Java Developer specializing in cloud-native microservices
Senior Full-Stack Engineer specializing in AI, SaaS, and IoT platforms
Mid-level Full-Stack Developer specializing in Node.js, TypeScript, and React/Next.js
Mid-level Full-Stack Engineer specializing in backend systems and AI integration
Mid-level Software Engineer specializing in Generative AI and cloud-native microservices
Mid-level Generative AI Engineer specializing in LLMs, RAG, and MLOps
Senior Full-Stack Engineer specializing in Python/Flask, React, and autonomous AI systems
Mid-level DevOps/Cloud Engineer specializing in AWS EKS and Terraform
Mid-level DevOps & Customer Success Engineer specializing in cloud, networking, and GenAI
Haeshitha Indukuri
Entry AI/ML Engineer specializing in Generative AI, LLMs, and MLOps
“Built and productionized a MediCloud/Medicoud LLM microservice platform that lets clinicians query medical data in natural language, orchestrating multi-step RAG-style workflows with LangChain and evaluating/debugging with LangSmith. Delivered measurable gains (consistency ~70%→90% / +20%; latency ~2.0s→1.1s / -40%) by implementing structured prompts, fallback logic across multiple LLMs, hybrid retrieval tuning, and AWS Lambda performance optimizations (package size, async, caching).”
Thimone Noupa
Mid-level DevOps Engineer specializing in AWS, Kubernetes, and CI/CD automation
“DevOps/cloud infrastructure engineer focused on AWS automation: built GitHub Actions/Jenkins pipelines for containerized deployments with strong security controls and rollback, and implemented Terraform-based AWS provisioning with modular code and remote state/locking. Has led on-prem to AWS migration cutovers with structured risk/rollback planning and stabilization, but has not worked directly with IBM Power/AIX/LPARs or PowerHA/HACMP.”
Senior Full-Stack AWS Developer specializing in cloud-native microservices and serverless systems
Tejaswi devarapalli
Junior Software Engineer specializing in backend systems and AI infrastructure
“Built both a full-stack AWS file-processing pipeline and a production AI document Q&A system ('smart-doc'). Stands out for combining strong cloud engineering with practical LLM/RAG architecture, including hybrid retrieval, reranking, structured outputs, confidence-based retries, and production monitoring.”