Vetted SQLAlchemy Professionals

Pre-screened and vetted.

DP

Mid Software Engineer specializing in FinTech backend systems

San Francisco, CA4y exp
StripeUniversity of Central Missouri
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SM

Mid-level Machine Learning Engineer specializing in NLP, federated learning, and fraud detection

CA, USA5y exp
AppleUSC
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RW

Staff SRE/Software Engineer specializing in cloud automation and distributed test systems

29y exp
AdvectaUniversity of San Francisco
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JL

Senior Full-Stack Python Engineer specializing in Django, microservices, and cloud platforms

San Diego, CA11y exp
DropboxJacksonville University
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SS

Mid-Level Software Engineer specializing in FinTech, treasury systems, and real-time data pipelines

San Francisco, CA6y exp
StripeUniversity of Memphis
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ST

Mid-level Full-Stack Python Developer specializing in cloud-native microservices

USA6y exp
ShopifyUniversity of Illinois Springfield
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NN

Mid-level Software Engineer specializing in scalable backend and cloud-native systems

New York, USA5y exp
MetaUniversity at Buffalo
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LS

Mid-level Python Backend Developer specializing in FinTech and ML-driven fraud detection

San Francisco, USA4y exp
StripeUniversity of North Carolina at Charlotte
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MG

Senior Full-Stack Engineer specializing in backend systems and cloud-native microservices

Pace, FL11y exp
micro1Florida Institute of Technology
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SS

Mid-Level Full-Stack Python Engineer specializing in AI-powered web apps and cloud-native systems

San Francisco, CA6y exp
StripeSaint Louis University
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PF

Senior Data Engineer specializing in BI Analytics and AI/ML

Lighthouse Point, FL11y exp
DropboxJacksonville University
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ST

Senior Software Engineer specializing in data engineering, BI analytics, and AI/ML

Moulton, AL11y exp
DropboxFlorida State University
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VJ

Mid-level Software Engineer specializing in cloud-native AI/ML and full-stack systems

Los Angeles, CA6y exp
NetflixFitchburg State University
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Pavan Devulapalle - Mid-level Software Engineer specializing in cloud platforms and AI-integrated full-stack development in Seattle, WA

Pavan Devulapalle

Screened ReferencesModerate rec.

Mid-level Software Engineer specializing in cloud platforms and AI-integrated full-stack development

Seattle, WA3y exp
AmazonUniversity of Texas at Dallas

Backend engineer who built Flask-based internal APIs supporting GenAI-driven provisioning/diagnostics (Outpost/AWS Outposts-like environment), with deep hands-on optimization across Postgres/SQLAlchemy (2s to <200ms endpoint improvement). Experienced integrating ML/LLM workflows via AWS SageMaker and Bedrock, and designing multi-tenant isolation plus high-throughput Redis-backed background task pipelines (minutes to seconds).

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SN

Mid-level AI/ML Engineer specializing in NLP, graph models, and MLOps for FinTech and Healthcare

Remote, USA5y exp
StripeKent State University

AI/ML engineer who has deployed production LLM/transformer-based systems for merchant intelligence and fraud/support optimization, delivering +27% merchant engagement and +18% payment success. Deep experience in privacy-preserving, PCI DSS-compliant data/ML pipelines (Airflow, AWS Glue, Spark, Delta Lake) and scalable microservices on Kubernetes, plus proven cross-functional delivery in healthcare claims analytics at UnitedHealth Group (12% HEDIS claim reduction).

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NM

Mid-level Full-Stack Python Developer specializing in cloud-native banking applications

6y exp
TruistPace University

Backend engineer who built a low-latency real-time transaction API in Python/Flask, with strong depth in PostgreSQL/SQLAlchemy performance tuning (time-based partitioning, indexing, connection pooling). Has production experience integrating ML scoring and OpenAI-style APIs with safety/latency controls, and designing multi-tenant isolation strategies including per-tenant pooling/caching and premium-tenant isolation.

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JR

Joseph Rivas

Screened

Senior AI/ML Engineer specializing in GenAI, MLOps, and computer vision

Boston, MA9y exp
Jaxon.AIGeorgia Tech

ML/AI engineer with hands-on ownership of production document intelligence and GenAI systems, spanning model experimentation, AWS deployment, monitoring, and iterative optimization. Stands out for turning document-heavy workflows into reliable, near real-time products with measurable gains in accuracy, latency, and manual-effort reduction, while also shipping citation-grounded RAG features that drove user trust and adoption.

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Harsh Sanas - Intern Full-Stack Engineer specializing in AI and distributed systems in Los Angeles, CA

Harsh Sanas

Screened

Intern Full-Stack Engineer specializing in AI and distributed systems

Los Angeles, CA2y exp
Scale AIUSC

Full-stack product engineer who has designed and shipped production web experiences in EV charging, trading, automotive companion apps, and AI systems. Stands out for owning user-facing React experiences through backend integration and production monitoring, with a strong bias toward reliability in real-time and high-stakes workflows. Also has early-stage Scale AI experience building a Text-to-SQL agent stack with Python, PostgreSQL, Redis, Kafka, and AWS.

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YY

Yuanhui Yang

Screened

Senior Software Engineer specializing in Python backend systems on AWS

Livermore, CA8y exp
ASMLShanghai Jiao Tong University

Backend/data engineer from ASML who modernized a legacy SAS-based statistical processing system into a cloud-native AWS platform (Lambda/FastAPI, Step Functions/EventBridge, Glue, S3/RDS) with strong reliability and data-quality practices. Demonstrated measurable performance wins (RDS query reduced from 90+ seconds to <5 seconds) and hands-on incident ownership for production ETL pipelines.

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AP

Senior Backend/Platform Engineer specializing in Python and AWS

Covington, Georgia, United State10y exp
CapgeminiGeorgia State University

Backend/data engineer with hands-on production experience across Python/FastAPI services and AWS (Lambda, API Gateway, SQS, ECS) delivered via Terraform and GitHub Actions. Built Glue-to-Redshift ETL pipelines with Step Functions retry/catch patterns, schema evolution safeguards, and data quality checks; also modernized a legacy SAS monthly reporting system into Python microservices with rigorous side-by-side parity validation. Demonstrated strong SQL tuning skills with a reported improvement from 5 minutes to 15 seconds.

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