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Vetted BigQuery Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in NLP, time-series forecasting, and edge AI
Executive Software Engineering Leader specializing in scalable SaaS and distributed systems
Mid-level Machine Learning Engineer specializing in healthcare risk prediction and GenAI
Mid-level Machine Learning Engineer specializing in forecasting, NLP, and MLOps
Mid-level AI/ML Engineer specializing in risk modeling, NLP, and Generative AI
Mid-level Machine Learning Engineer specializing in Generative AI, NLP, and recommender systems
Mid-level Data Scientist specializing in GenAI, NLP, and cloud MLOps
Mid-level Machine Learning Engineer specializing in MLOps and LLM/RAG systems
Mid-level Data Scientist specializing in ML, NLP, and forecasting across finance and retail
Junior Data & AI Analyst specializing in BI, LLM applications, and analytics
Senior DevOps & Cloud Engineer specializing in Kubernetes, IaC, and DevSecOps
Mid-level Data Engineer specializing in cloud data platforms and BI analytics
Senior AI/ML Engineer specializing in MLOps and Generative AI (LLMs/RAG)
Senior DevOps/Site Reliability Engineer specializing in multi-cloud Kubernetes platforms
Senior Full-Stack Java Engineer specializing in cloud microservices and FinTech/insurance platforms
Tushar Jayendra Mhatre
Intern Data Scientist/ML Engineer specializing in generative AI and ML platforms
“AI Engineering Intern at The Etherloop building the backend for a healthcare lifestyle recommendation app, including a multi-agent RAG-based system that uses curated SME data plus web search to generate personalized supplement recommendations from user lifestyle details and blood biomarkers. Evaluates against 500+ SME ground-truth profiles with ranking metrics and focuses on HIPAA-aligned deployment, privacy/security, and guardrails to reduce hallucinations and unsafe outputs.”
Bharath Reddy Nallu
Mid-level Machine Learning Engineer specializing in NLP and scalable MLOps
“Data/ML engineer in financial services (Northern Trust) who built a production RAG-based LLM system to connect structured transaction/portfolio data with unstructured market and internal documents for risk teams. Strong in end-to-end pipelines (AWS Glue/Airflow/PySpark), entity resolution, and taking models from prototype to reliable daily production with performance tuning (LoRA + TensorRT) and monitoring.”