.png){kind=logo}
Vetted XGBoost Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in NLP, recommender systems, and on-device ML
Mid-Level Full-Stack Software Engineer specializing in FinTech and cloud-native AI systems
Senior Product and Data Leader specializing in AI, experimentation, and e-commerce growth
Mid-level AI/ML Engineer specializing in GPU-accelerated LLM and vision systems
Senior AI/ML Engineer specializing in personalization, recommendations, and forecasting
Mid-level Business Analyst specializing in data analytics and financial systems
Mid-level Full-Stack Python Engineer specializing in AI search and distributed systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and GPU-accelerated cloud systems
Mid-level Generative AI & Machine Learning Engineer specializing in LLMs and RAG
Senior Data Engineer specializing in cloud data platforms and real-time analytics
Junior AI Software Engineer specializing in LLM systems and retrieval (RAG)
Mid-level AI/ML Engineer specializing in LLMs, ranking systems, and MLOps
Principal Data Scientist / AI Engineer specializing in healthcare-native AI platforms
Alexander Choy
Director of AI/ML Engineering specializing in MLOps, data platforms, and 3D computer vision
“Backend/data engineer focused on production ML/LLM systems: built a real-time FastAPI inference API on Kubernetes with strong reliability patterns (timeouts, idempotent retries, centralized error handling). Delivered AWS platforms using EKS + Lambda with GitHub Actions/Helm CI/CD and built Glue-based ETL from S3/Kafka into Snowflake with schema evolution and data-quality controls; also modernized legacy analytics/recommendation workflows into Python services with safe, feature-flagged cutovers.”
Yifei Tang
Intern Machine Learning Engineer specializing in vision-language models and robotics
“Robotics software engineer with hands-on experience building a vision-guided grasping pipeline on a 7-DOF Franka arm, implementing gradient-based IK with null-space optimization and RRT* motion planning in ROS1. Strong in sim-to-real deployment and real-world debugging—addressed frame misalignment via hand-eye calibration and centralized TF configuration, and reduced replanning/jitter by tuning a weighted pose filter using rosbag replay and variance/grasp-time metrics. Also built an ESP32-based mobile robot architecture combining embedded decision-tree control with WiFi/web high-level commands.”
Gowri Kajipuram
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multimodal deep learning
“ML/LLM engineer who has built and productionized a large multimodal LLM pipeline end-to-end—fine-tuning a 20B+ parameter model with distributed/FSDP training and deploying on Kubernetes via Triton for ~5x throughput. Strong focus on reliability and safety (monitoring with SHAP, guardrails, A/B testing) with reported ~22% relevance lift and reduced harmful/incorrect outputs, plus experience orchestrating ETL/retraining workflows with Airflow across S3/Snowflake/RDS.”
Rajan Souda
Mid-level AI Engineer specializing in Generative AI and MLOps
“Built and deployed a production LLM-powered clinical support assistant at BJC HealthCare (RAG + transformer) to answer patient questions, summarize clinical notes, and support appointment workflows. Implemented PHI-safe data pipelines (Spark/Hadoop/Kafka) with automated scrubbing, dataset versioning, and audit logs, and runs the system on Docker/Kubernetes with Pinecone vector search while partnering closely with clinical operations staff.”