Vetted XGBoost Professionals

Avni Tripathi

Screened ReferencesModerate rec.

Mid-level Data Scientist specializing in NLP, RAG, and information retrieval for RegTech

Gurgaon, India5y exp

“Built and deployed a production document Q&A/research platform that combines semantic search (vector DB embeddings) with structured knowledge-graph querying to reduce analyst research time. Used in high-stakes domains like Politically Exposed Person profiling and extracting critical information from ESG/regulatory documents, with a human-in-the-loop evaluation process (precision@k and source-text highlighting) to ensure accuracy.”

“ML practitioner who built a production customer risk scoring system to replace slow manual approvals, owning the full pipeline from feature engineering and XGBoost training to deploying a Dockerized FastAPI prediction service. Emphasizes reliability and business-aligned evaluation (recall/ROC-AUC, threshold tuning, drift monitoring) and is comfortable translating model decisions into stakeholder metrics like conversion rate (experience at EasyBee AI).”

MohammadHossein Karimian

Screened

Mid-level Front-End Developer specializing in React and TypeScript

Regina, SK5y exp

“Frontend engineer who has led end-to-end builds of complex React + TypeScript workflow editors (multi-step scenario builder with nodes/connections/conditions) with strong quality practices (CI/CD, unit tests, schema validation, logging, feature flags). Also delivered an AR flower-placement feature during an internship at Ecomspiders, rebuilding the experience with Three.js, live camera preview, and surface placement tested across devices and lighting conditions.”

Shehab mohamed mohamed

Screened

Junior AI Engineer specializing in MLOps, LLMs, and multi-agent systems

Cairo, Egypt2y exp

“ML/AI engineer focused on production-grade systems, with experience building a low-latency multi-agent 'neural concierge' booking platform used across domains like restaurants and hospitals. Also worked on a healthcare computer vision system for nystagmus/eye-movement analysis, showing a mix of scalable LLM infrastructure, MLOps, and safety-conscious medical AI experience.”

“Built a voice-driven desktop assistant for users with mobility impairments, integrating Whisper and Google Gemini and adding voice-authentication via speaker embeddings for secure command execution. Has hands-on experience with AWS serverless/microservices patterns (Lambda, S3, CloudFront, CloudWatch) and CI/CD, plus built an internal MySQL-to-MongoDB migration tool used by the CTO and dev team with an emphasis on safe, low-impact data transformation.”

SaiDheerajReddy Gadikota

Screened

Mid-level AI Engineer specializing in agentic systems and enterprise LLM platforms

USA, USA4y exp

“Current AI engineer at a startup who has spent the last year architecting multi-agent systems for software development workflows. Stands out for combining LLM speed with engineering discipline—using tools like Pydantic, LangGraph, and LangChain to build reliable, production-ready agent workflows with validation, routing, and retry logic.”

Dhairya Gajjar

Screened

Mid-Level Software Engineer specializing in Healthcare Data Platforms

Remote2y exp

“Backend/ML engineer with healthcare domain experience building secure Medicare/Medicaid data APIs and real-time patient risk scoring. Shipped an end-to-end ML pipeline (scikit-learn/XGBoost) served via SageMaker and integrated into Flask APIs, with strong production reliability practices (Kafka schema validation, regression replay, observability, drift monitoring, and human-in-the-loop guardrails).”

Sai Vamsee Bandaru

Screened

Entry-level Data Analyst and AI Engineer specializing in machine learning and LLM systems

New York, NY1y exp

“Founding-engineer-oriented full-stack product engineer who built an AI tutor system end-to-end, spanning React UI, FastAPI backend, retrieval/LLM pipelines, and Postgres optimization. Stands out for combining product thinking with deep systems work: improving onboarding and activation, shipping quickly with beta users, and abstracting reusable retrieval infrastructure for multiple use cases.”

“Backend/ML engineering candidate focused on fintech automation who architected a zero-to-one agentic/LLM-enabled system to reconcile messy financial documents and bank transactions, reporting ~40% operational efficiency gains. Experienced migrating monoliths to event-driven microservices with incremental rollout via reverse proxy, and implementing production-grade security (OAuth2/JWT, RBAC, Supabase RLS) plus resilience patterns (timeouts/retries under concurrency).”

Ekta Mulkalwar

Screened

Junior Machine Learning & Full-Stack Engineer specializing in applied AI systems

Los Angeles, CA2y exp

“Master’s thesis focused on building and deploying a gait-based biometric authentication system using mobile accelerometer time-series data as an alternative to passwords/2FA. Emphasized real-world robustness by addressing sensor noise and variability (phone placement, walking speed, footwear) and improving safety using biometric metrics like FAR/FRR and EER, while collaborating closely with a non-ML thesis advisor.”

Gowtham Manikanta Chilukabathula

Screened

Entry-level AI and Data Analyst specializing in LLMs and analytics

Memphis, TN1y exp

“Candidate brings a blend of AI, analytics, and go-to-market support experience through an AI/data internship and graduate assistant role. They analyzed data across 50+ organizations to identify high-fit outreach segments, improving targeting efficiency by about 28%, and also built/reviewed GPT-4 and LangChain-based outbound messaging systems with strong quality controls.”