Vetted Docker Professionals

Pre-screened and vetted.

CB

Mid-level Software Developer specializing in backend and full-stack enterprise applications

USA4y exp
Accenture
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DD

Engineering Leader specializing in FinTech, payments, and enterprise platforms

23y exp
QRails
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JM

Mid-level Cloud Security & DevSecOps Engineer specializing in AWS/Azure security automation

Atlanta, Georgia7y exp
WEG Electric Corp.
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JD

Mid-level Finance Systems Analyst specializing in ERP controls, revenue modeling, and BI

Dallas, TX7y exp
Human Appeal
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RD

Senior QA Engineer specializing in test automation, API validation, and CI/CD quality

St. Louis, MO9y exp
KnowInk
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KS

Senior Full-Stack Java Engineer specializing in cloud microservices and FinTech/insurance platforms

Chicago, IL6y exp
State Farm
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NM

Senior Frontend & Full-Stack Engineer specializing in SaaS and real-time web applications

Stockton, CA3y exp
BP
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AB

Mid-Level Full-Stack Java Developer specializing in Spring Boot microservices and Angular

Riverwoods, IL7y exp
Discover
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KS

Senior SDET specializing in mobile, telecom, and test automation

Irvine, CA9y exp
Take2 Consulting
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AE

Senior DevOps/Cloud Engineer specializing in Azure, Kubernetes, and CI/CD

8y exp
Cape Fear Valley Health System
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KA

Mid-level Backend Software Engineer specializing in Java, Spring Boot, and AWS

Dallas, TX5y exp
Paycom
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JS

Director of Information Security and Security Engineer specializing in cloud compliance

12y exp
Lavender
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TJ

Tushar Jayendra Mhatre

Screened ReferencesStrong rec.

Intern Data Scientist/ML Engineer specializing in generative AI and ML platforms

Remote4y exp
The Aether LoopUniversity of Oklahoma

AI Engineering Intern at The Etherloop building the backend for a healthcare lifestyle recommendation app, including a multi-agent RAG-based system that uses curated SME data plus web search to generate personalized supplement recommendations from user lifestyle details and blood biomarkers. Evaluates against 500+ SME ground-truth profiles with ranking metrics and focuses on HIPAA-aligned deployment, privacy/security, and guardrails to reduce hallucinations and unsafe outputs.

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TG

Tushar Gwal

Screened ReferencesStrong rec.

Mid-level AI/ML Engineer specializing in GenAI, computer vision, and MLOps

Tallahassee, FL4y exp
Product Manager AcceleratorIllinois Institute of Technology

AI engineer with experience taking a GPT-4-powered GenAI career coach toward production on Azure AI Foundry, re-architecting the backend with hybrid (vector + keyword) search and RAG optimizations to cut latency by 50%. Also has client-facing TCS experience building healthcare ETL pipelines and delivering error-free monthly reports, plus current work analyzing agentic system reasoning traces and guardrail drift as an AI research fellow.

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BR

Bharath Reddy Nallu

Screened ReferencesStrong rec.

Mid-level Machine Learning Engineer specializing in NLP and scalable MLOps

4y exp
Northern TrustUniversity of the Cumberlands

Data/ML engineer in financial services (Northern Trust) who built a production RAG-based LLM system to connect structured transaction/portfolio data with unstructured market and internal documents for risk teams. Strong in end-to-end pipelines (AWS Glue/Airflow/PySpark), entity resolution, and taking models from prototype to reliable daily production with performance tuning (LoRA + TensorRT) and monitoring.

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BK

Bhuvaneswari Kudaravalli

Screened ReferencesStrong rec.

Mid-Level Full-Stack Software Engineer specializing in TypeScript, React/Next.js, and Node/Nest APIs

Portland, OR5y exp
Portland State UniversityPortland State University

Full-stack engineer who built and scaled an AI-powered web product (React/Next.js + TypeScript/NestJS) with MongoDB, Redis, and RabbitMQ. Strong in rapid iteration while maintaining production quality—uses versioned APIs, feature flags, CI/CD, and observability (correlation IDs/structured logs) to ship frequently and debug distributed workflows. Also created an internal operations dashboard for real-time visibility and control of background jobs/AI workflows that was adopted quickly by ops and product teams.

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Urvish Shah - Mid-level Robotics Software & Systems Engineer specializing in ROS2 multi-robot autonomy in Buffalo, NY

Urvish Shah

Screened ReferencesStrong rec.

Mid-level Robotics Software & Systems Engineer specializing in ROS2 multi-robot autonomy

Buffalo, NY4y exp
Indian Institute of Technology GandhinagarUniversity at Buffalo

Robotics software engineer with ROS2 multi-robot experience spanning decentralized signal source localization (LoRa RSSI on TurtleBot3) and a master’s-thesis project on collaborative object transportation with 4 robots. Strong in sim-to-real debugging—implemented noise modeling (RBF) and practical hardware/coordination fixes (CoG tuning, clock sync/flags) to make algorithms work reliably on real robots.

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Bhavesh Ittadwar - Senior Full-Stack Engineer specializing in scalable web and cloud systems in USA

Bhavesh Ittadwar

Screened ReferencesStrong rec.

Senior Full-Stack Engineer specializing in scalable web and cloud systems

USA3y exp
Heartland Community NetworkNorth Carolina State University

JavaScript engineer who built a Michelin-specific headless CMS forms platform based on apostrophe-forms, powering forms across 400+ Michelin websites. Designed an extensible, SOLID-aligned modular field architecture with a shared design system, cutting hundreds of lines of per-project code across 10+ implementations while driving cross-device compatibility and performance (BrowserStack, Lighthouse, SSR).

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VS

Venkata Siva Sai Prathyush Kolli

Screened ReferencesStrong rec.

Intern Robotics Software Engineer specializing in ROS2 multi-robot autonomy

Newark, DE1y exp
University of DelawareUniversity of Delaware

Robotics intern at the University of Delaware who built and debugged ROS2-based multi-robot coordination systems, focusing on real-time reliability (timestamp alignment, latency/jitter instrumentation, QoS/executor tuning). Also improved SLAM stability by fixing LiDAR/encoder synchronization and tuning state-estimation parameters, with a simulation-first workflow using Gazebo and Docker/CI for reproducible deployments.

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PK

Praniket Ketan Walavalkar

Screened ReferencesStrong rec.

Junior AI Software Engineer specializing in RAG agents and cloud data platforms

Seattle, WA1y exp
University of WashingtonUniversity of Washington

AI Software Engineer (student employee) at University of Washington IT who helped deploy "Purple," a governed, explainable LLM platform on Azure used by 100,000+ students/faculty/staff. Independently led scalable reliability efforts by building automated agent quality/load/red-team testing and CI/CD health validation (Playwright/Node.js, Azure DevOps), and previously built an explainable AI scheduling assistant for clinical operations at Proliance Surgeons.

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SP

Soumya Perala

Screened ReferencesStrong rec.

Senior DevOps Engineer specializing in cloud infrastructure and CI/CD automation

Sunnyvale, CA9y exp
Wide Open WestSan José State University

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.

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AK

Alifya Kitabi

Screened ReferencesStrong rec.

Senior QA Engineer specializing in SaaS payments and legal tech

San Diego, CA22y exp
8amNJIT

QA professional from fintech/SAP security and complex identity systems who has owned end-to-end testing across the SDLC, including being the sole QA on a high-risk payment platform carrier migration. Demonstrated strength in integration testing, data integrity validation, and diagnosing calculation/automation defects using controlled test data and scripted date emulation; experienced with JIRA/TestRail and Selenium-based regression coverage.

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RZ

Rui Zhao

Screened ReferencesStrong rec.

Junior Machine Learning Engineer specializing in semantic search and retrieval systems

Los Angeles, CA1y exp
University of Southern CaliforniaUSC

Built and shipped a production RAG system (“TROJAN KNOWLEDGE”) for answering questions over technical PDFs, using a 3-stage retrieval stack (BM25 + FAISS + cross-encoder) to lift F1 from 71% to 84%. Drove major performance gains with a 3-level cache (memory/Redis/disk) cutting latency from ~200ms to ~10ms, and added Prometheus/Grafana monitoring plus LangChain-based fallback logic to handle OpenAI rate limits under load.

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