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Vetted Version Control Professionals
Pre-screened and vetted.
Austin Ha
Senior Software Engineer specializing in AR/VR and real-time interactive systems
“Senior engineer (8 years) with experience at Facebook/Meta and Apple, spanning design tooling and XR/enterprise device experiences. Built the Origami component marketplace end-to-end (packaging node logic + metadata, SQL/JSON storage, filtering/search, local caching) with versioning and forward-compatibility decisions, and has a strong track record of UX-focused robustness improvements and safe rollouts via feature flags and telemetry.”
Staff Software Engineer specializing in FinTech and distributed systems
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Senior VR Engineer specializing in Unity game systems and multiplayer experiences
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Staff Software Engineer specializing in AI infrastructure and distributed systems
Senior AI/ML Engineer specializing in Generative AI, RAG, and MLOps for FinTech
Principal Site Reliability Engineer specializing in cloud, DevSecOps, and platform reliability
Jeffrey Xuzhang
Junior Software Engineer specializing in full-stack and cloud systems
“Worked on an AWS DynamoDB Journal team project building internal operator dashboards end-to-end, creating Java/Spring Boot APIs and integrating them into a Spring Boot/Thymeleaf/JavaScript UI to speed up debugging workflows. Has experience with data-heavy web apps and performance techniques (load balancing, caching, pagination, compression) plus hands-on debugging across unit/integration/E2E tests; also maintained and enhanced a React website at Global Spark.”
Cody Christ
Senior Software Engineer specializing in AI, full-stack platforms, and real-time systems
“Built end-to-end AI analytics experiences spanning React/TypeScript, serverless APIs, and Postgres, with a strong focus on streaming UX, observability, and reliability. Stands out for turning ambiguous AI product ideas into shippable MVPs, then abstracting repeated patterns into reusable orchestration and multi-tenant configuration systems that improved speed, consistency, and maintainability.”
Senior Full-Stack AI Engineer specializing in generative AI and cloud platforms
Jayaprakash Majji
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and scalable inference
“ML/LLM engineer who built and shipped an LLM-powered internal knowledge assistant at Meta, focusing on production-grade RAG to reduce hallucinations and improve trust. Deep experience with scaling and serving (FSDP/DeepSpeed/LoRA, Triton, Kubernetes autoscaling) and reliability practices (Airflow retraining, MLflow versioning, monitoring with rollback), including sub-100ms latency and ~35% GPU memory reduction.”
Brian Gomez
Senior Machine Learning Engineer specializing in AI/ML, NLP, and computer vision
“McKinsey & Company ML/NLP practitioner who builds production-grade AI systems across sectors (notably healthcare and finance), including RAG/LLM solutions, entity resolution pipelines, and embedding-powered search with vector databases. Demonstrated measurable impact (40% reduction in data duplication) and strong MLOps/data workflow practices (Airflow, MLflow, Spark, AWS/GCP, Prometheus, CI/CD).”
Senior Machine Learning Engineer specializing in LLMs and scalable MLOps
Senior Full-Stack Engineer specializing in React/TypeScript, React Native, and LLM-enabled products
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable MLOps
Mid-level AI/ML Engineer specializing in LLMs, multilingual NLP, and low-latency MLOps
Senior AI/ML Engineer specializing in LLM agents, RAG, and production ML systems
Sai Chandra Bandi
Mid-level AI/ML Engineer specializing in Generative AI, LLM alignment, and RAG
“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”