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Vetted Software Engineers in Washington
Pre-screened and vetted in Washington.
Mid-Level Software Engineer specializing in distributed systems and AI agent platforms
Senior Full-Stack Engineer specializing in AI/ML, LLMs, and RAG systems
Osvaldo Calles
Senior Software Engineer specializing in developer tools, cloud automation, and generative AI
“Built and deployed a production chatbot on osvaldocalles.com and iterated through real-world LLM engineering issues: model quota/cost tradeoffs (migrating to Nova Pro), RAG accuracy via semantic chunking, AWS IAM/guardrail/security pitfalls, and Lambda/API Gateway streaming constraints (prefers JS for streaming layer). Experienced with agent orchestration using Strands SDK (AWS-focused) and LangGraph (Vercel/container deployments), plus evaluation pipelines using LLM-as-evaluator, dashboards, and staged model rollouts.”
Chinmay Wadgaonkar
Mid-level Software Engineer specializing in Windows graphics performance and cloud automation
“Graphics software engineer with academic robotics/HRI experience at Oregon State University under Dr. Heather Knight, leading a ROS+Python physical robot and Unity/C# VR system to study how motion/texture/collisions are perceived in VR (2 papers + thesis). Also built ROS-based Wizard-of-Oz TurtleBot study systems and multi-robot coordination experiments, plus industry experience with Docker/Kubeflow ML tooling and Azure DevOps CI/CD automation.”
Grey Luo
Junior Software Engineer specializing in LLM agents and AWS backend systems
“Built and owned the end-to-end architecture for a Quick Flows “research card” backend at AWS, using an event-driven AWS stack (SNS/SQS, DynamoDB, S3) to support asynchronous research output processing and status tracking. Emphasized maintainability via unit tests, smoke tests, and CI/CD with staged environments (devtest and gamma).”
SAI RAM VARMA Budharaju
Mid-level Software Engineer specializing in backend, distributed systems, and ML-integrated platforms
“Built and shipped production AI systems spanning customer support automation at Uber, privacy-preserving federated health modeling on iOS, and an open-source semantic search layer for Postgres. Stands out for combining strong LLM/product instincts with rigorous eval design, measurable production impact, and zero-to-one execution across backend, mobile, and developer infrastructure.”
Senior Software Engineer specializing in distributed systems, AI/ML platforms, and cloud-native SaaS
Senior Applied Scientist specializing in LLMs, GenAI, and agentic systems
Senior Software Engineer specializing in AI platforms for healthcare and industrial time-series ML
Staff AI/ML Engineer specializing in NLP, recommender systems, and Generative AI
Senior QA/Test Engineer specializing in cross-platform testing and identity, automotive, and consumer apps
Mid-level Backend/Distributed Systems Engineer specializing in cloud observability and data ingestion
Mid-level Backend/Data Engineer specializing in AWS data pipelines and scalable services
Junior Software Engineer specializing in AWS cloud infrastructure and ML systems
Mid-Level Full-Stack Software Engineer specializing in AWS and automation
Mid-Level Software Engineer specializing in cloud platforms, ML/GenAI, and distributed systems
Mid-level AI/Software Engineer specializing in NLP pipelines and LLM-driven automation
Paul Liu
Senior Software Engineer specializing in cloud-native backend and web/mobile apps
“Backend engineer with Tesla experience building Python-based, serverless microservices for a supply chain portal, including a MongoDB-backed tracking/logging system and a reconciler Lambda to manage retries and failures. Has hands-on Kubernetes (EKS) and GitOps (Argo CD) experience, plus real-time Kafka pipelines for fleet/IoT telemetry and proxy-based migrations from monolith systems to AWS databases.”
Madhusmita Oke
Mid-Level Software Engineer specializing in cloud-native distributed systems
“Gameplay engineer with hands-on ownership of a real-time C++ combat ability system, including diagnosing and eliminating large-scale combat frame spikes by refactoring hit detection to an event-driven, animation-notify approach (cut collision checks ~80%). Also implemented UE5 networked abilities (dash) with client-side prediction and server-authoritative reconciliation, plus projectile ballistics validated through debug spline visualizations and unit tests.”