Vetted SQLAlchemy Professionals

“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

Screened

Mid-level AI/ML Engineer specializing in LLMs, MLOps, and cloud-native ML

San Antonio, TX5y exp

“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.”

“Built and owned an enterprise AI document-processing deployment at an automotive tech startup, taking it from discovery to stabilization. Strong in production LLM/RAG systems and backend reliability, with measurable impact including 8,000+ documents processed monthly and turnaround time reduced from nearly 24 hours to about 3 hours.”

“Backend engineer with experience in both healthcare (Siemens) and payments (Bitwise), focused on scaling Python APIs and modernizing architectures. Has led monolith-to-microservices migrations and introduced Kafka async processing, Redis caching, and ELK observability, citing ~40% faster issue resolution and improved reliability via idempotency and strong security controls (OAuth2/JWT, RBAC, RLS).”

“ML engineer/data scientist who built and deployed a real-time fraud detection platform at Citi on AWS SageMaker, processing 3M+ daily transactions and improving fraud response by 28%. Combines unsupervised anomaly detection (autoencoders) with ensemble models (XGBoost/Random Forest) plus Airflow/Step Functions orchestration, drift monitoring, and explainability (SHAP) to keep models reliable and compliant in production.”

“Robotics-focused full-stack engineer with hands-on ROS experience building sensor-processing and control nodes, plus a track record of debugging and optimizing real-time robot responsiveness via profiling and message-timing analysis. Uses Webots for pre-hardware validation and Docker/CI/CD to standardize deployments and catch issues early.”

Austin Near

Screened

Mid-Level Software Engineer specializing in full-stack and backend systems

Santa Clara, CA3y exp

“Full-stack JavaScript developer in small-company environments building PCB manufacturing web tooling. Owned and delivered blob-storage upload/download infrastructure (including an internal developer library) and a training/compliance tracking tool. Implemented secure, broadly compatible SSO for a customer portal under a <1 month deadline tied to an 8-figure customer deal, despite having no prior authentication experience.”

Rajeshwar Peri

Screened

Mid-level Data Analyst specializing in healthcare and financial analytics

Chicago, IL5y exp

“Healthcare analytics candidate with hands-on experience turning messy claims and CRM data into validated reporting tables, automating monthly reporting in Python/Airflow, and operationalizing churn metrics in SQL and Tableau. They appear especially strong in stakeholder-aligned metric design and delivered a reported ~10% churn reduction through cohort analysis, segmentation, and at-risk member targeting.”

“Analytics engineer/data professional with strong healthcare and membership analytics experience, combining SQL, dbt, BigQuery, Python, and Tableau to turn messy source data into trusted executive reporting. Stands out for metric governance and stakeholder alignment work, including unifying conflicting business definitions and delivering a CMS market-risk model that identified $792M in excess payer costs.”