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Vetted XGBoost Professionals
Pre-screened and vetted.
Senior AI/ML Engineer specializing in LLMs, NLP, and production MLOps
Mid-Level Generative AI Engineer specializing in LLM apps, RAG, and cloud deployment
Mid-level Data Scientist specializing in ML, NLP, and cloud deployment
Mid-level Data Scientist / ML Engineer specializing in NLP, GenAI, and cloud ML deployment
Mid-level Data Engineer specializing in cloud data platforms for Healthcare and Financial Services
Mid-level Data Engineer specializing in cloud lakehouse and real-time streaming
Mid AI/ML Engineer specializing in MLOps, deep learning, and cloud ML systems
Senior Data Scientist and AI Engineer specializing in NLP, LLMs, and MLOps
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Intern Data Scientist specializing in machine learning and predictive modeling
Mid-level AI Engineer specializing in production LLM, RAG, and agentic AI systems
Senior Data Scientist specializing in NLP, MLOps, and cloud ML platforms
Principal Automation Architect specializing in cloud DevOps, microservices, and MLOps
Senior AI Python Engineer specializing in Generative AI and MLOps
Pravallika Kilari
Mid-level AI/ML Engineer specializing in NLP, GenAI, and MLOps in healthcare and finance
“AI/ML engineer with CVS Health experience deploying production LLM systems in regulated healthcare settings, including a large-scale RAG solution (1M+ documents) built for compliance-grade, auditable policy/regulatory Q&A with strong anti-hallucination controls. Also delivered an NLP summarization system for physician notes/case narratives by partnering closely with non-technical care operations stakeholders and iterating via prototypes, dashboards, and feedback loops.”
GOWRI SHANKAR ANANTHULA
Mid-level Data Scientist & Generative AI Engineer specializing in LLMs and RAG
“ML/NLP practitioner who built a retrieval-augmented generation (RAG) system for large financial and operational document sets using Sentence-Transformers (all-mpnet-base-v2) and a vector DB (e.g., Pinecone), with a strong focus on retrieval evaluation and chunking strategy optimization. Experienced in entity resolution (rules + embedding similarity with type-specific thresholds) and in productionizing scalable Python data workflows using Airflow/Dagster and Spark.”
Ishwar Girase
Mid-level AI/ML Engineer specializing in LLMs, GenAI, and NLP
“AI/ML Engineer who built a production RAG-based LLM system for insurance policy documents, turning thousands of messy PDFs into a searchable index using LangChain, Azure AI Search vectors, hybrid retrieval, and FastAPI. Strong focus on evaluation (MRR/precision@k/recall@k, REGAS) and performance optimization (vLLM), with prior clinical NLP experience using BERT-based NER validated on ground-truth datasets.”
Ruudra Patel
Junior Data Scientist specializing in ML, LLMs, and RAG applications
“University hackathon finalist (2nd place) who built CareerSpark, a production-style multi-agent career guidance app in 24 hours using a hierarchical debate architecture with a moderator/judge agent. Has startup internship experience at LiveSpheres AI using LangChain for multi-LLM orchestration, and demonstrates a structured approach to testing/evaluation (golden sets, integration sims, latency/accuracy KPIs) plus strong non-technical stakeholder communication.”