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Vetted SQLAlchemy Professionals
Pre-screened and vetted.
Principal Python Architect specializing in cloud-native platforms, DevOps, and MLOps
Senior Software Engineer specializing in data ingestion, web crawling, and ETL pipelines
Senior Backend Engineer specializing in Python microservices, Kubernetes, and streaming systems
Senior Software Engineer specializing in data pipelines and financial data platforms
Mid Software Engineer specializing in distributed systems and full-stack platforms
Principal Backend Software Engineer specializing in JVM microservices on AWS
Senior Backend Engineer specializing in Python microservices and cloud platforms
Mid-Level Full-Stack Software Engineer specializing in cloud-native web applications
Junior Full-Stack Software Engineer specializing in AWS serverless and Python
Mid-level Full-Stack Software Developer specializing in cloud-native microservices
Mid-level Python Developer specializing in AWS cloud-native backend systems
Mid-level AI/ML Engineer specializing in NLP, fraud detection, and LLM applications
Mid-Level Software Engineer specializing in backend, microservices, and cloud platforms
Senior Data Scientist specializing in ML engineering and cloud analytics
Soumya Perala
Senior DevOps Engineer specializing in cloud infrastructure and CI/CD automation
“Backend/platform engineer who has owned a real-time data ingestion/processing/reporting API built with FastAPI, Redis, and Celery, including performance tuning via query/index optimization, caching, and async workers. Strong Kubernetes + CI/CD + GitOps (ArgoCD) experience, plus hands-on monitoring/logging (Prometheus/Grafana/ELK) and a Kafka/Spark real-time streaming project from their master’s program.”
Siva Pothuru
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and cloud-native ML
“LLM/agent engineer at USAA who built a production GPT-4o RAG conversational assistant for financial analysts, focused on regulatory interpretation and internal documentation search. Emphasizes compliance-grade reliability with strict grounding, safe fallbacks, and full auditability via MLflow/DVC plus human-in-the-loop review; reports ~45% reduction in ticket resolution time.”