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Vetted Amazon S3 Professionals

Pre-screened and vetted.

Amazon S3DockerPythonCI/CDAWSPostgreSQL
RM

Ricardo Montes

Senior Software QA Engineer specializing in test automation and API validation

10y exp
Education DynamicsUC Santa Cruz
SeleniumTestNGJUnitPostmanSwaggerJira+52
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VF

Van Ferguson

Senior Software Engineer & Engineering Lead specializing in Unreal/Unity VR training and multiplayer

San Diego, CA23y exp
AmazonNational University
AgileAndroidAWSCI/CDC#C+++53
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DP

Dr.AdamPage Page

Executive AI Architect specializing in low-power edge/embedded AI systems

Austin, TX18y exp
AMBiQUniversity of Maryland, Baltimore County
BudgetingGenerative AIRetrieval-Augmented Generation (RAG)Performance optimizationPyTorchTensorFlow+118
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PN

Phani Narla

Junior Data Engineer specializing in cloud ETL/ELT and lakehouse platforms

Newark, NJ2y exp
Horizon Blue Cross Blue Shield of NJUniversity of Central Missouri
Amazon CloudFrontAmazon DynamoDBAmazon EC2Amazon EMRAmazon KinesisAmazon Redshift+94
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SM

Saba Mushtaq

Senior Full-Stack Engineer specializing in Python, cloud-native microservices, and APIs

United States7y exp
Allscripts
PythonNode.jsJavaScriptTypeScriptDjangoFlask+38
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SK

Shashidhar Katkam

Senior Full-Stack & Cloud Engineer specializing in AWS Serverless SaaS

9y exp
Republic Services
JavaScriptTypeScriptPythonReactNext.jsReact Native+76
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AZ

Aideen Zane

Senior Full-Stack Software Engineer specializing in Python, React, and LLM-powered applications

Woodbridge, Virginia, US7y exp
HealthEdge
PythonDjangoFlaskFastAPIReactNext.js+87
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SK

Sai Krishna Sriram

Screened ReferencesStrong rec.

Mid-level Generative AI & ML Engineer specializing in production LLM and RAG systems

Temecula, California3y exp
CLD-9University of Colorado Boulder

“AI/ML engineer who shipped a production blood-test report understanding and personalized supplement recommendation product, using a LangGraph multi-agent pipeline on AWS serverless with OCR via Bedrock and RAG over vetted clinical research. Also built end-to-end recommender system pipelines at ASANTe using Airflow (ingestion, embeddings/features, training, registry, batch scoring/monitoring) with KPI reporting to Tableau, with a strong focus on safety, evaluation, and measurable reliability.”

PythonRSQLScalaPySparkPyTorch+179
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DK

Dhruv Kamalesh Kumar

Screened ReferencesStrong rec.

Mid-level Generative AI Engineer specializing in LLM agents and RAG applications

Boston, MA4y exp
Burnes Center for Social ChangeNortheastern University

“GenAI builder and technical lead with ~2 years of hands-on production experience, including GENIE (a GenAI sandbox for ~44,000 Massachusetts public-sector employees) and A-IEP, a multilingual platform helping parents understand complex IEP documents (cut processing from ~15 minutes to ~2 and used by 1,000+ parents). Strong in RAG/agentic architectures, AWS serverless + Step Functions orchestration, and rigorous evaluation/guardrails for reliable real-world deployments.”

Generative AILarge Language Models (LLMs)Retrieval-Augmented Generation (RAG)Prompt EngineeringOpenAI APILangChain+96
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RG

Rithiwik Garuda

Screened ReferencesStrong rec.

Mid-level Full-Stack Engineer specializing in cloud-native, event-driven data platforms

5y exp
InfosysUniversity of New Haven

“Backend/data engineer with hands-on production experience building Python (FastAPI/Flask) data enrichment services secured with Okta OAuth2 and monitored via Splunk/Dynatrace. Has delivered AWS event-driven and data-migration solutions (Lambda + Kafka to EKS; Glue from on-prem Oracle to S3/data lake) and modernized Informatica match/merge logic to cloud services using parallel-run parity validation and stakeholder sign-off.”

AgileAmazon EKSAmazon S3AngularApache KafkaArgo CD+124
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PR

Priyanka Ramesh

Screened ReferencesStrong rec.

Junior Full-Stack Software Engineer specializing in Java/Spring Boot and React

Boston, MA2y exp
IpserLabNortheastern University

“Backend engineer (IpserLab) who owned Python services for a production quiz/analytics platform, focusing on reliability and low-latency behavior under peak load. Hands-on with Kubernetes + Docker deployments and GitHub Actions CI/CD in a GitOps-style workflow, including solving configuration drift and enabling fast rollbacks. Also implemented Kafka-based event streaming with idempotent consumers and strong observability (lag tracking, structured logging, alerting).”

ReactTypeScriptAngularSASSBootstrapResponsive Design+243
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SR

Sandeep Reddy

Screened ReferencesStrong rec.

Mid-level SRE/DevOps Engineer specializing in cloud infrastructure automation and Kubernetes

Jacksonville, Florida5y exp
Transcor Data ServicesFlorida International University

“Cloud/SRE-style engineer at TDS supporting revenue-critical transportation SaaS platforms on AWS/GCP with Kubernetes. Has hands-on experience leading high-impact production work including DDoS mitigation, zero-downtime MSSQL→PostgreSQL migration using CDC, and building secure GitHub Actions + ArgoCD delivery pipelines and Terraform-based GKE infrastructure.”

AgileAnsibleApache TomcatArgo CDAuto ScalingAWS+163
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JS

Joshua Sylvester

Screened ReferencesStrong rec.

Senior Machine Learning Researcher/Engineer specializing in temporal modeling and production ML systems

8y exp
Darwin Deason Institute for Cybersecurity (SMU)Southern Methodist University

“Backend engineer who built and evolved a startup data-processing backend (Express.js/MySQL) handling millions of user data points, with a microservices pipeline integrating multiple social media APIs. Emphasizes reliability and security through comprehensive testing, robust error/retry handling for sequential pagination constraints, and tight IAM/JWT/OAuth-based access controls.”

PythonNode.jsCC++AngularReact+131
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VV

varsha viswanathan

Screened ReferencesStrong rec.

Entry-Level Software Engineer specializing in backend systems and FinTech

Fremont, CA1y exp
UnicgateUniversity of Texas at Dallas

“Software engineering intern experience at Zoho Corp and Zeus Desk building and deploying customer-facing systems. Delivered a real-time booking platform backend that stayed stable for 1,000+ users by optimizing MySQL queries/indexing and shipping hotfixes during production latency incidents. Also integrated financial operations APIs across 50+ small-bank partners by creating a normalization/validation layer to handle inconsistent partner data and prevent integration breakages.”

PythonJavaC++CGoJavaScript+117
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PT

Preet Talati

Screened ReferencesStrong rec.

Junior Full-Stack Developer specializing in .NET Core and React

2y exp
Defense Language InstituteUniversity of Illinois Chicago

“Full-stack engineer who has shipped end-to-end, customer-facing features in an education/student-population context, including an authorized incident-reporting workflow with RBAC and a PostgreSQL-backed data model. Has 0→1 React+TypeScript/Redux product experience (CTO at Jiffur) and hands-on production operations with CI/CD (Azure Pipelines/GitHub Actions), including resolving a real CPU-saturation outage via operational mitigation and scaling changes.”

MVCC#MicroservicesSQLMySQLNoSQL+122
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LD

Linda DN

Screened ReferencesStrong rec.

Senior Cloud DevOps Engineer specializing in AWS architecture, IaC, and DevSecOps

Lawrenceville, GA11y exp
Madison LogicDelta State University

“DevSecOps/AWS infrastructure engineer at Madison Logic who owns a 15-account AWS footprint and treats nearly all AWS resources as code (Terraform/CloudFormation). Led a CI/CD platform migration (Bitbucket → GitLab + GitHub Actions) supporting WordPress and containerized microservices, improving release frequency to weekly/daily, and has hands-on production incident response experience on ECS Fargate using Datadog with fast rollback via immutable ECR tags and task definition revisions.”

Microsoft AzureDevOpsCI/CDTerraformAWS CloudFormationGitHub Actions+233
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RR

Rachid Raphael Sassine

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in cloud infrastructure and ETL pipelines

Horsham, PA4y exp
HCLTechWestern Governors University

“Works on clinical trial applications and data pipelines, including AWS Lambda-based file transfer workflows for clinical study metadata. Has hands-on experience hardening production systems by adding observability, SSH/auth exception handling (Paramiko), retries/timeouts, and validating changes across SIT/UAT/prod. Also supports adoption through tailored technical demos for new teams and vendor partners integrating into their workflows.”

AgileAWSAWS LambdaBashCI/CDChange Management+103
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HJ

Harshal J Hirpara

Screened ReferencesStrong rec.

Mid-level Machine Learning Engineer specializing in LLM alignment and applied reinforcement learning

Mountain View, CA3y exp
QuinUniversity of Illinois Chicago

“AI/LLM engineer who has shipped production systems end-to-end, including a note-taking product (Notey) combining audio/image capture, ASR, summarization, and a semantic chat agent over past notes. Also has applied ML experience in healthcare, collaborating directly with doctors to validate an EEG seizure-detection pipeline, and uses Kubernetes to optimize GPU usage for LLM training.”

PythonTypeScriptC++JavaScalaShell Scripting+135
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DW

Diya Wadhwani

Screened ReferencesStrong rec.

Mid-level Full-Stack Software Engineer specializing in MERN, AWS, and secure authentication

4y exp
One CommunityNortheastern University

“Application-layer full-stack engineer who has shipped enterprise-facing integrations and developer tooling, including an end-to-end Slack integration for automated ticket creation and a real-time feature-flag dashboard (React/TS + GraphQL/Apollo + NestJS) with audit trails. Has hands-on AWS container operations experience (ECS Fargate/ALB/RDS) and has improved product performance (35% faster dashboards) while building auth and RBAC for 500+ users.”

TypeScriptPythonJavaCC++Kotlin+123
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PY

Pallavi Yellisetty

Screened ReferencesModerate rec.

Mid-level AI/ML Engineer specializing in predictive modeling, NLP, and recommender systems

Bristol, PA4y exp
DermanutureUniversity of Texas at Arlington

“AI/ML manager who has deployed production NLP in healthcare—mining unstructured clinical notes and combining them with structured patient data to predict readmissions, with strong emphasis on data alignment and terminology normalization. Also experienced operationalizing ML with Airflow/MLflow and AWS Step Functions/SageMaker, plus stakeholder-facing Power BI dashboards (e.g., marketing customer segmentation).”

A/B TestingAgileAmazon EC2Amazon S3Amazon SageMakerAnomaly Detection+90
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NT

Nikhil Tatikonda

Screened ReferencesModerate rec.

Intern AI/ML Engineer specializing in LLM agents, RAG, and automation workflows

Buffalo, NY1y exp
ColaberryUniversity at Buffalo

“AI automation builder who shipped an OpenAI-powered weekly "trending AI tools" WoW reporting system (65 categories) that reduced a 6–7 hour manual process to ~10 minutes at negligible API cost. Also building a RAG-based content creation prompt engine that turns PDFs into storyboards with fact-checking/traceback to source lines, plus experience with AWS deployment components (Lambda, ECR, App Runner, Bedrock, API Gateway) and GitHub Actions.”

AlgorithmsAmazon API GatewayAmazon S3API DevelopmentChatGPTClaude+100
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AN

Abhishek Namdev Sawant

Screened ReferencesModerate rec.

Mid-Level Backend Software Engineer specializing in Java microservices and cloud platforms

Seattle, WA5y exp
Ecological Servants ProjectSeattle University

“Backend/platform engineer with payments and insurance domain experience (Cognizant), owning high-volume production systems end-to-end. Shipped a Spring Boot payment tokenization service with strong observability and phased migration that cut transaction latency ~30% and improved payment efficiency ~25%. Also productionized an ML-driven financial health/risk analytics pipeline with near real-time dashboards across 70+ schools, emphasizing interpretability, data quality, and drift monitoring.”

AgileAmazon EC2Amazon S3Amazon SQSAngularAPI Design+139
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