.png){kind=logo}
Vetted Data Preprocessing Professionals
Pre-screened and vetted.
Mid-level AI Engineer specializing in NLP, MLOps, and predictive analytics
Mid-level Backend Software Engineer specializing in cloud-native microservices
Senior Python Backend Engineer specializing in AWS, APIs, and data pipelines
Mid-level Software Engineer specializing in full-stack, ML, and cloud
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and cloud-native MLOps
Junior Data Scientist specializing in NLP, OCR, and recommendation systems
Mid-level Machine Learning Engineer specializing in LLM/VLM inference and RAG systems
Mid-level Software Engineer specializing in microservices, cloud, and FinTech systems
Intern Full-Stack/AI Engineer specializing in LLM applications and cloud-native web systems
Senior Machine Learning Engineer specializing in GenAI and LLM-powered systems
Mid-level Data Scientist specializing in NLP, risk analytics, and MLOps
Mid-level AI/ML Engineer specializing in risk modeling, NLP, and Generative AI
Intern Game Systems Designer specializing in combat systems and real-time graphics
Mid-level Applied AI Engineer specializing in Generative AI and RAG systems
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Rodolfo Lopez
Senior Math Educator transitioning to Data Science & Business Analytics
“Recent McCombs School of Business (UT Austin) Post Graduate Program graduate in Data Science & Business Analytics with hands-on project experience spanning stock clustering/segmentation and hotel booking-cancellation prediction. Strong in end-to-end analysis workflows (EDA, cleaning, feature engineering) and rigorous model comparison/selection, with exposure to boosting methods and imbalanced-data techniques; limited experience so far with embeddings/vector databases and production deployment.”
Sudheer koki
Mid-level AI/ML Engineer specializing in predictive modeling, data pipelines, and RAG systems
“Built and productionized an LLM-powered internal knowledge search system in a regulated environment, using embeddings/vector DB retrieval with strict grounding and confidence gating to reduce hallucinations. Reported ~45% accuracy improvement over keyword search and implemented end-to-end orchestration, monitoring, CI/CD, and incremental re-indexing to manage latency and data freshness while driving adoption with business stakeholders.”
Nagasaikumar Jampani
Mid-level AI/ML Engineer specializing in Generative AI and RAG pipelines
“AI/LLM engineer with healthcare domain experience who built a production clinical support “chart bot” for Molina, including PHI-safe ingestion of 200k+ PDF policies, vector retrieval, and a fine-tuned LLaMA served via vLLM on ECS Fargate. Demonstrated measurable performance wins (HNSW + namespace partitioning; 30% inference latency reduction) and a rigorous evaluation/monitoring approach, while partnering closely with nurses and operations teams to shape workflows and guardrails.”