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Vetted XGBoost Professionals
Pre-screened and vetted.
Mid-level AI & Machine Learning Engineer specializing in computer vision and MLOps
Mid-level AI/ML Engineer specializing in production ML, NLP, and computer vision
Mid-level AI/ML Engineer specializing in NLP, Computer Vision, and Generative AI
Staff Machine Learning Engineer specializing in Generative AI, MLOps, and Computer Vision
Mid-level Data Scientist specializing in GenAI, NLP, and agentic RAG systems
Principal AI Platform Architect specializing in agentic AI and enterprise LLM infrastructure
Mid-level AI/ML Engineer specializing in GenAI, MLOps, and big data on cloud platforms
Intern AI/ML Engineer specializing in generative AI and multimodal agentic systems
Senior AI/ML Engineer specializing in production AI systems for healthcare and finance
Mid-level AI Data Engineer specializing in real-time streaming and LLM-powered fraud analytics
Mid-level Generative AI Engineer specializing in LLMs, NLP, and multimodal systems
Ashutosh Choudhari
Mid-level AI/ML Engineer specializing in LLM applications and cloud-native systems
“LLM engineer who has shipped production AI systems, including an RFP requirements extraction platform (OpenAI o4-mini + Azure AI Search + FastAPI) achieving 90%+ accuracy and ~5x throughput through grounding, structured outputs, parallelization, and caching. Also partnered with legal/compliance stakeholders at Nexteer Automotive to deliver an AI document comparison tool with traceability and confidence indicators, adopted by non-technical users and saving ~2 FTEs of review time.”
Nandini Kosgi
Mid-level AI/ML Engineer specializing in LLMs, RAG, and fraud/risk analytics in Financial Services
“Built and shipped a production-grade GenAI Fraud & Compliance Investigation Copilot for a large US bank, integrating OCR docs, structured data, and prior case history to generate grounded, regulator-friendly summaries and red-flag highlights. Demonstrates strong end-to-end LLM systems engineering (LangGraph/LangChain, hybrid retrieval with FAISS+BM25, guardrails/citations, streaming/latency optimization) plus rigorous evaluation and close partnership with compliance stakeholders.”
Sahithi S
Mid-level AI/ML Engineer specializing in NLP, MLOps, and Generative AI
“Built and deployed a production generative AI chatbot at NVIDIA using LangChain + GPT-3 integrated with internal data sources, cutting response time nearly in half and improving CSAT by ~12 points. Also delivered LLM-driven QA tools by fine-tuning Hugging Face transformer models and deploying via an AWS-based pipeline (Lambda/Glue/S3) with orchestration (Airflow/Step Functions), CI/CD, Kubernetes, and monitoring (MLflow/Splunk/Power BI).”
Leela Tikkisetty
Mid-level Software Engineer specializing in ML platforms and cloud-native backend systems
“Software engineer with experience at Google and the City and County of San Francisco building production AI systems, including a RAG-based internal support chatbot and ML-driven ticket priority tagging. Has scaled data/ML platforms with Airflow on GCP (1M+ records/day, 99.9% SLA) and deployed multi-component systems with Docker and Kubernetes (GKE), using modern LLM tooling (LangChain/CrewAI, Claude/OpenAI, Pinecone/ChromaDB, Bedrock/Ollama).”
Byron Pineda
Staff/Lead Data Scientist specializing in Generative AI, NLP/LLMs, and MLOps
“Lead Data Scientist (10+ years) with recent work in healthcare data: built production pipelines that unify EHR, genomics, and clinical notes using NLP (spaCy/BERT/BioBERT) and scalable Spark-based processing. Also led development of domain-specific LLM/NLP systems for chatbots and semantic search, deploying models via FastAPI/Flask and improving retrieval with FAISS-backed, fine-tuned clinical embeddings and RAG-style workflows.”
Rushi Reddy Lambu
Mid-level AI/ML Engineer specializing in Generative AI and MLOps
“GenAI/LLM engineer and architect who built and deployed a production generative AI financial forecasting and scenario analysis platform at McKinsey, leveraging Claude (Anthropic), LangChain, Airflow, MLflow, and AWS SageMaker. Demonstrates strong LLMOps/MLOps rigor (monitoring, drift detection, automated retraining) and deep experience implementing global privacy controls (GDPR, differential privacy, audit trails) while partnering closely with finance executives and legal/IT stakeholders.”