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Vetted Amazon Bedrock Professionals
Pre-screened and vetted.
Senior Software Engineer specializing in AI platforms for healthcare and industrial time-series ML
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and MLOps
Senior Software Engineer specializing in cloud-native microservices and AI-enabled products
Senior Full-Stack Software Engineer specializing in cloud-native microservices and AI/LLM integration
Director-level Data Engineering Leader specializing in AI/LLM platforms and real-time data systems
Executive Software Engineering Leader specializing in digital transformation and omnichannel commerce
Senior Full-Stack Engineer specializing in Python, React, and AI-powered cloud applications
Senior Data Scientist specializing in AI/ML platforms for finance and healthcare
Mid-level AI/ML Engineer specializing in LLM RAG pipelines and cloud MLOps
Senior Staff Full-Stack Engineer specializing in AI copilots and cloud platforms
Mid-level Machine Learning Engineer specializing in GenAI, forecasting, and MLOps
Senior Full-Stack Python Developer specializing in cloud, data platforms, and GenAI
Mid-level AI/ML Engineer specializing in cloud MLOps and GenAI for fraud detection
Tianyi Wang
Entry-Level Backend/Cloud Engineer specializing in distributed systems and AI platforms
“Full-stack engineer with deep serverless AWS experience who built VidToNote, an AI video analysis platform, end-to-end using Next.js App Router/TypeScript and an event-driven pipeline (API Gateway, Lambda, DynamoDB, S3, Step Functions, SQS). Strong on production reliability and observability (CloudWatch, X-Ray, structured logging), plus data/analytics work in Postgres with measurable query optimizations and durable LLM evaluation workflows. Amazon background; integrated 22 AWS services and completed AWS Solutions Architect Professional certification within a month.”
Sri Charan Reddy Mallu
Mid-Level Software Development Engineer specializing in GenAI and full-stack cloud systems
“Full-stack engineer with experience across Magna, C3.ai, and Amazon, building GenAI-enabled products and finance transaction systems. Has shipped Next.js (App Router) + TypeScript features backed by Go/Python RAG pipelines, and emphasizes production quality via load testing, Selenium regression coverage, LLM-aware integration testing, and Azure observability. Also built LangGraph-orchestrated multi-step content generation workflows with robust retry/idempotency strategies.”
Yongyan Cao
Principal Vehicle Dynamics & Control Systems Engineer specializing in autonomous driving and hybrid powertrains
“Robotics controls engineer with experience spanning an RV/trailer automatic hitching and towing robot (vision + EKF sensor fusion, anti-jackknife/anti-sway, multi-loop torque assistance control) and 3 years on a ROS-based RoboTaxi autonomous driving stack at Pegasus Technology. Improved MPC trajectory generation robustness by converting hard constraints to soft constraints with slack variables, and built an AI-powered PR review agent (Claude-code) integrated into CI/CD to reduce bugs.”
Praveen V
Mid-Level Software Engineer specializing in Generative AI and RAG systems
“Built a production RAG-based natural-language-to-SQL system at Global Atlantic to replace slow, expensive manual analytics ticket workflows, focusing heavily on retrieval quality and measurable evaluation (200-question ground-truth set; recall@5 improved 0.65→0.78 via semantic chunking). Also built a custom MCP-style agent orchestrator for a personal project (arxiv-ai) to improve flexibility and Langfuse-aligned observability, and has hands-on experience with LangGraph, CrewAI, and n8n.”
Akshit Gaur
Mid-level AI Engineer specializing in agentic LLM systems
“Built and productionized a dual-agent LLM invoice-processing system for GFI Partners, adding guardrails and audit trails to earn stakeholder trust and drive adoption while cutting operational burden by 75%. Uses LangSmith observability to diagnose real-time workflow regressions and has experience teaching agentic AI concepts (e.g., at Carnegie Mellon) through hands-on, scaffolded demos.”