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Vetted scikit-learn Professionals
Pre-screened and vetted.
Mid-level AI Data Scientist specializing in financial risk, fraud detection, and NLP/LLM systems
Mid-Level Software Engineer specializing in cloud-native distributed systems
Mid-level AI/ML Engineer specializing in MLOps, distributed ML, and RAG pipelines
Mid-level Data Scientist specializing in marketing analytics and scalable data platforms
Senior Machine Learning Engineer specializing in NLP, Generative AI, and healthcare/legal AI
Senior Data Scientist specializing in Generative AI, NLP, and MLOps
VP Data Engineer specializing in AI-driven analytics platforms for investment management
Mid-level Data Engineer specializing in AI/ML data platforms and real-time streaming
Mid-level Data Engineer specializing in cloud lakehouse and streaming pipelines
Senior DevOps Engineer / AWS Solutions Architect specializing in Kubernetes and DevSecOps
Mid-level AI Backend Engineer specializing in LLM applications and scalable ML systems
Senior AI Platform Engineer specializing in agentic AI and RAG systems
Director of Engineering specializing in AI/ML, data platforms, and consumer messaging
Mid-level Software Developer specializing in backend cloud and API platforms
Senior AI/ML Engineer specializing in GenAI, LLMs, NLP, and MLOps
Executive Engineering Leader specializing in Telehealth Platforms and Healthcare IT
Abhishikth Meesala
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and fraud detection
“At PwC, built and productionized an agentic RAG enterprise search assistant over 6M internal documents (8M embeddings), deployed across AWS and GCP. Drove major retrieval gains (72%→92% precision via BM25+dense hybrid with RRF and cross-encoder re-ranking), reduced hallucinations 30%, achieved <2s latency at 50–60K queries/month, and cut support tickets 30%—boosting adoption to 2,500 users by adding source-cited answers.”
JYOTHI N
Senior Data Scientist specializing in analytics, experimentation, and BI on AWS
“Data/ML practitioner focused on healthcare data quality and record linkage: analyzed 10M+ records, built anomaly detection and NLP-driven entity resolution, and automated AWS ETL/validation pipelines (Glue/Redshift/Lambda), cutting data errors by 40% and generating $500k in annual savings. Has hands-on experience with embeddings (Sentence Transformers/spaCy), FAISS vector search, and fine-tuning for domain-specific matching.”