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Vetted Data Pipelines Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in NLP, LLMs, and fraud/AML analytics
Mid-level Machine Learning Engineer specializing in healthcare risk prediction and GenAI
Mid-level Data Scientist specializing in pricing, revenue optimization, and operational efficiency
Senior AI Infrastructure Engineer specializing in cloud-native and edge AI systems
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 ML, MLOps, and applied risk modeling
Mid-level Data Scientist specializing in recommendations, search relevance, and NLP
Mid-Level Full-Stack Software Engineer specializing in distributed systems and FinTech
Mid-level Software Engineer specializing in FinTech data pipelines and backend systems
Mid-level Backend Software Engineer specializing in Java microservices and cloud-native systems
Mid-level Full-Stack Java Developer specializing in cloud microservices and React
Mid-level Data Engineer specializing in cloud-native ETL/ELT and Snowflake analytics platforms
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Senior Data Strategy & AI Product Consultant specializing in analytics platforms and privacy-safe measurement
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.”
Pranali Redij
Marketing Analytics & Marketing Operations professional specializing in CRM and automation
“Lifecycle/CRM marketer with hands-on HubSpot expertise who improves funnel performance by fixing data foundations (workflow logic + validation), then building lifecycle segmentation, nurture sequences, and measurement dashboards tied to CAC/ARR. Has driven measurable lifts in data accuracy, early lifecycle engagement, and lead qualification through targeted messaging and A/B-tested email optimization across companies including Wicket and Beekind.”
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.”
Sai Manikanta Kasireddy
Mid-level Machine Learning Engineer specializing in cloud-native GenAI and RAG systems
“Built and productionized an internal GenAI chatbot that makes company policy/SOP knowledge instantly searchable, using a secure RAG architecture on AWS (Bedrock/Titan embeddings/OpenSearch Serverless, Textract/Lambda/S3 ingestion, Claude 3 Sonnet). Demonstrates strong MLOps/orchestration experience (Airflow, Step Functions with Lambda/Glue/SageMaker) and a rigorous reliability approach (RAGAS metrics, A/B testing, citation validation, monitoring), including collaboration with compliance stakeholders via review dashboards.”
Venkata Siva Sai Prathyush Kolli
Intern Robotics Software Engineer specializing in ROS2 multi-robot autonomy
“Robotics intern at the University of Delaware who built and debugged ROS2-based multi-robot coordination systems, focusing on real-time reliability (timestamp alignment, latency/jitter instrumentation, QoS/executor tuning). Also improved SLAM stability by fixing LiDAR/encoder synchronization and tuning state-estimation parameters, with a simulation-first workflow using Gazebo and Docker/CI for reproducible deployments.”