.png){kind=logo}
Vetted Git Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Junior Software Engineer specializing in AI/ML and recommendation systems
Executive AI/ML Engineer specializing in LLMs, NLP, and production ML systems
Staff Software Engineer specializing in ML infrastructure and data platforms
Senior AI Engineer specializing in NLP and large language models
Richard Jackson
Senior Machine Learning Engineer & Solution Architect specializing in cloud AI systems
“Backend/ML platform engineer with Google experience leading Python microservices for an AI-driven recommendation/retrieval system, including PyTorch inference and a retrieval-augmented generation workflow. Strong in production Kubernetes + GitOps (ArgoCD), real-time Kafka/Spark pipelines, and phased on-prem/legacy to AWS/GCP cloud migrations with reliability-focused rollout and rollback practices.”
Senior Software Engineer specializing in Java backend and OLAP systems for Ads platforms
Senior AI/ML Engineer specializing in LLM applications, RAG systems, and MLOps
Mid-Level Software Engineer specializing in Search, Ads, and Shopping systems
Senior Software Engineer specializing in full-stack systems and ML-driven platforms
Senior Data Engineer specializing in cloud data platforms and real-time streaming
Staff Full-Stack Software Engineer specializing in cloud platforms and healthcare data pipelines
Senior VR Engineer specializing in Unity game systems and multiplayer experiences
Senior Full-Stack Software Engineer specializing in AI-powered distributed systems
Senior Full-Stack Engineer specializing in AI and GenAI platforms
Nathan Telles
Staff Software Engineer specializing in distributed systems, cloud platforms, and AI services
“Meta engineer who owned end-to-end production systems for AI-enabled smart glasses, spanning React/TypeScript apps through Node/Java microservices on AWS EKS with Kafka/Postgres. Built and productionized a real-time RAG pipeline (LangChain + OpenAI + Elasticsearch) with rigorous guardrails (shadow/canary, fallbacks, monitoring), delivering major improvements in latency (~35–40%), error reduction (~30%), and engagement (reported +40% DAU).”
Upender Mula
Mid-level Software Engineer specializing in backend systems, real-time data pipelines, and FinTech
“Backend/platform engineer who has owned real-time reporting and streaming analytics systems end-to-end, combining FastAPI/Postgres APIs with Kafka consumers, Celery background jobs, and Redis caching. Strong DevOps/GitOps experience deploying Python/Node microservices to AWS EKS with Helm, ArgoCD/FluxCD, and CI pipelines, and has supported phased on-prem to AWS migrations using Terraform and traffic cutovers.”
Arjun Dubashi
Director-level Software Development Manager specializing in AWS agentic AI and logistics platforms
“Software development manager who led development of the open-source AI agents framework Strands (for Amazon Bedrock), which launched mid-year and is used as a primary agent-development framework at Amazon and externally. Owned the Strands SDK end-to-end, drove adoption through stakeholder management and agile execution, and led a JavaScript-to-TypeScript migration to improve type safety and maintainability.”
Danny Anop
Senior Full-Stack Engineer specializing in SaaS, e-commerce, and frontend platforms
“Frontend-leaning full-stack engineer who has built a multi-tenant AI-powered widget and admin dashboard platform used across 40+ merchant websites. Strong in TypeScript/Next.js/GraphQL systems design, reusable platform primitives, and cross-layer debugging, with a clear track record of shipping scalable product experiences under ambiguity.”
Kevin Wang
Senior Software Engineer specializing in cloud infrastructure and distributed systems
“Amazon engineer focused on productionizing LLM-powered developer workflows, including code assistance, debugging automation, and internal AI tooling. Stands out for combining hands-on ML systems work with strong platform engineering, including an orchestration engine that reportedly saved about $10K/day and reduced a manual workflow from 12 hours to under a second.”
Esha Pahwa
Intern Machine Learning Engineer specializing in LLM agents and multimodal reasoning
“LLM/agent engineer who built a production code-generation agent at Corvic AI that lets non-technical users query CSV/tabular data in natural language by generating and executing Python. Focused on making LLM systems reliable and scalable via schema-aware validation, sandboxed execution-feedback retries, prompt caching/embeddings, async execution, and high-throughput data processing with Polars; also partnered with Adobe product/marketing to ship brand-aligned AI content generation for email and push notifications.”