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Vetted Python Developers in New York
Pre-screened and vetted in New York.
Senior Backend/Platform Engineer specializing in Python, Kubernetes, and data streaming
Mid-level Python Full-Stack Developer specializing in FinTech and real-time data/ML systems
Swathi Sankaran
Senior Python Full-Stack Developer specializing in cloud, data engineering, and ML/GenAI
“Backend/data engineer with hands-on production experience building FastAPI services on AWS and implementing strong reliability/observability (CloudWatch, ELK, correlation IDs, alarms). Has delivered serverless + container solutions with IaC (CloudFormation/Terraform) and Jenkins CI/CD, and built AWS Glue/PySpark pipelines into S3/Redshift with schema-evolution and data-quality safeguards; demonstrated large-scale SQL tuning (45 min to 3 min on a 500M-row workload).”
Senior Full-Stack Engineer specializing in Python APIs and AI/ML integration
Senior Full-Stack Python Developer specializing in Flask/Angular and cloud microservices
Senior Python Full-Stack Developer specializing in cloud, MLOps, and GenAI
Pavan Yarlagadda
Junior Robotics Software Engineer specializing in ROS2 autonomy
“Graduate student researcher on the EARTH project (college collaboration with Moog) working on robotics for an arm/bucket system. Implemented waypoint-based path planning, built an Apriltag data pipeline, and developed ROS 2 tooling including a joystick-to-DeltaCAN teleop node; exploring reinforcement learning policies trained from Tera simulator + ROS 2 bag data to optimize trajectory planning under varying pressure/load conditions.”
Dhruv Kumar
Senior Backend Developer specializing in Python and AWS cloud-native systems
“Backend/data engineer with production experience building Python FastAPI services and AWS-native data pipelines. Has delivered containerized and serverless workloads (ECS/EKS/Lambda) with Terraform-based IaC, strong reliability patterns (JWT/RBAC, retries/circuit breakers, observability), and AWS Glue ETL into S3/Redshift. Demonstrated measurable SQL performance wins (40–50s to <4s) and owned real pipeline incidents through detection, mitigation, and prevention.”