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Vetted Amazon EKS Professionals
Pre-screened and vetted.
Staff Full-Stack Software Engineer specializing in distributed systems and healthcare platforms
Senior Full-Stack Software Engineer specializing in web platforms and AI/ML-enabled services
Senior Software Engineer specializing in telehealth and e-commerce platforms
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and scalable inference
Mid-level Full-Stack Software Engineer specializing in FinTech analytics and security
Senior Full-Stack Software Engineer specializing in FinTech payments and risk systems
Senior Software Engineer specializing in Python, cloud infrastructure, and AI-powered search
Mid-level Software Engineer specializing in backend APIs, data pipelines, and cloud microservices
Mid-level Software Engineer specializing in full-stack and distributed backend systems
Senior Full-Stack & AI/ML Engineer specializing in cloud-native SaaS and IoT analytics
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Senior Cloud Engineer specializing in AWS/Azure infrastructure, DevOps, and cloud-native platforms
Abhijay Sai Paladugu
Junior Data Scientist specializing in LLM agents, RAG, and reinforcement learning
“McKinsey practitioner who built and deployed production LLM systems for consultants/clients, including a Power BI-integrated multi-agent chatbot (RAG + text-to-SQL + formatting) with custom Python orchestration, verification loops, and a 100+ case eval set achieving ~95% consistency. Also delivered a taxonomy-mapper agent that standardized inconsistent labeling for C-suite stakeholders, cutting a process from >2 weeks to <30 minutes through demos and business-focused communication.”
Kevin Lee
Senior Backend Engineer specializing in Python and AWS serverless/data pipelines
“Serverless-focused backend/data engineer who has delivered production Python services on AWS (FastAPI on Lambda/API Gateway) plus Glue-based ETL pipelines from S3 to relational databases. Strong in operational reliability (timeouts, retries, monitoring/alerts) and modernization work, including parallel-run parity validation for migrating legacy batch logic to Python services. Demonstrated measurable SQL tuning impact (15 min to under 3 min).”
Ronald Nap
Intern Machine Learning & AI Engineer specializing in computer vision and ML systems
“Robotics/ML engineer with internship experience at Valeo building a deep-learning prototype to replace parts of a legacy SLAM backend for autonomous parking, focused on making models run reliably in real time on embedded hardware (quantization/distillation + TensorRT). Also brings strong MLOps/deployment experience (Docker, Kubernetes on AWS EKS, CI via GitHub Actions) and has supported patent filing by explaining the technical approach to legal stakeholders.”
Kevin Kyi
Intern Machine Learning Engineer specializing in RAG systems and AWS cloud infrastructure
“Internship at BlueFoxLabs building and deploying an AI/ML RAG system for a biopharma client on top of LibreChat, including an AWS Textract ingestion pipeline and PGVector retrieval deployed to AWS EKS. Demonstrated production-minded scalability work by moving from a vertically scaled EC2 setup to a horizontally scaling Kubernetes/EKS deployment, using CI/CD to safely incorporate requirement changes like tabular document data.”
Yihao Xie
Senior Backend Engineer specializing in Python and AWS serverless systems
“Backend/data engineer with Amazon supply-chain experience building production serverless Python services and ETL pipelines on AWS (Lambda, API Gateway, S3, RDS, Glue). Has modernized legacy SAS jobs into Python with rigorous parity testing and phased migrations, and has delivered major SQL performance gains (minutes down to seconds) through indexing and partitioning.”