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Vetted Data Analysts
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in MLOps, NLP, and real-time data pipelines
Mid-level Data Analyst specializing in predictive analytics and cloud data engineering
Mid-level Data Analyst specializing in CRM and business intelligence analytics
Senior Software Engineer specializing in backend systems, AI/ML, and cloud infrastructure
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and cloud-native MLOps
Mid-level Data Engineer specializing in cloud ETL and healthcare data platforms
Junior Mechatronics/Robotics Engineer specializing in computer vision and automation
Mid-level Data Analyst specializing in financial and healthcare analytics
Mid-level Data Scientist specializing in LLMs and applied machine learning
Mid-level Data Scientist specializing in pricing, revenue optimization, and operational efficiency
Mid-level AI/ML Engineer specializing in LLM training, evaluation, and applied mathematics
Mid-level Data Engineer specializing in cloud data platforms and BI analytics
Mid-level Business Intelligence Engineer specializing in AI-powered analytics
Junior Software Engineer specializing in backend systems and logistics data pipelines
Likitha Kukunarapu
Mid-level Applied AI Engineer specializing in data engineering and healthcare AI
“Built production LLM agents spanning document Q&A, financial insight generation, and ERP-like operational data workflows, with a strong focus on reliability, grounding, and evaluation. Stands out for translating LLM systems into measurable business outcomes, including 70%–80% support workload reduction and a fallback-rate improvement from 18% to 8% through targeted RAG iteration.”
Mid-level Business Analyst specializing in data analytics and process improvement
Rakesh Deshalli Ravi
Mid-level Data Science & AI Engineer specializing in LLMs and cloud ML platforms
“Built and deployed an LLM-powered mental health therapy assistant at AppHealth that segments users by stress level and delivers personalized, non-medical guidance. Implemented healthcare-focused safety guardrails (secondary LLM output filtering) and a multi-agent router workflow validated via statistical tests and therapist review, then scaled training/inference on AWS (EC2/Lambda/DynamoDB) with Kubernetes.”
Sushmitha Ramesh
Mid-level Data Scientist specializing in ML, LLMs, and Azure MLOps
“Cloud/ML engineer with production deployment experience on Azure (Dockerized models, managed APIs, data pipelines) who has repeatedly stabilized unreliable systems—e.g., taking an API-driven analytics pipeline from ~60% to 98% reliability and an Azure ML service from ~80% to 97% by addressing rate limits, container memory, and gateway timeouts. Also built an explainable contract-risk model for entertainment bookings (Transformers + SHAP) and integrated it into a legacy booking system via a Flask REST API, plus prior IoT work at Nissan processing CAN bus sensor streams for diagnostics/anomaly insights.”