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Vetted pandas Professionals
Pre-screened and vetted.
Mid-level Data Analyst specializing in analytics, dashboards, and growth insights
Mid-level AI/ML Engineer specializing in FinTech and production ML systems
Senior Software Engineer specializing in full-stack .NET and AI/ML systems
Senior Software Engineer specializing in full-stack systems and cloud platforms
Intern Business Analyst specializing in finance, operations, and data analysis
Junior Software Engineer specializing in AI-powered backend and distributed systems
Senior Full-Stack Python Developer specializing in microservices, data engineering, and cloud
Senior Full-Stack Python Engineer specializing in scalable web apps and APIs
Naresh Vemula
Mid-level Data Engineer specializing in Cloud & Big Data ETL/ELT
“Data engineer in financial services (Northern Trust) who has worked across ingestion, transformation, data quality, orchestration, and serving on AWS (S3/Glue/EMR) with Airflow. Highlights include processing ~15M transactions with validation/anomaly detection for regulatory reporting and improving Snowflake query performance by 27% for risk/compliance reporting. Also built a personal real-time streaming service (FastAPI, Kafka, Redis, Cassandra) and uses production reliability patterns like blue-green/atomic swaps and robust retry strategies.”
Sree Gautham Alluri
Mid-level Robotics & AI Developer specializing in autonomous navigation and LLM-powered robotic systems
“Robotics Support Engineer at HAI Robotics supporting a 385-robot warehouse fleet at a Shein client site. Built a production automation and reporting workflow to diagnose and resolve abnormal shelf locations, cutting incidents from ~250/day to ~25/day while providing actionable root-cause data to client/ops/maintenance. Hands-on ROS 2 (Humble) debugging across Nav2/localization/TF and sensor integration issues including QoS and firmware coordination.”
Rangel Koli
Entry-level Software Engineer specializing in backend and cloud systems
“Backend engineer who built and scaled a zero-to-one social product backend using Supabase (Postgres, Edge Functions, Auth, Realtime) plus Neo4j for graph-based friend recommendations. Demonstrates strong production rigor: staged rollouts with metrics, incident rollback/postmortems, and complex schema refactors using expand-contract/dual-write with reconciliation and feature flags. Notably proactive about edge cases like geo-boundary realtime delivery and idempotent retry safety.”