Vetted Containerization Professionals

Pre-screened and vetted.

CG

Mid-level AI/ML Engineer specializing in cloud MLOps, LLM agents, and risk & fraud modeling

5y exp
CNA FinancialUniversity of Dayton
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SV

Mid-level Software Development Engineer specializing in backend systems and data pipelines

5y exp
CignaUniversity of Texas at Arlington
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SC

Senior Software Engineer specializing in Unity, multiplayer, and AI/LLM systems

6y exp
Misapplied SciencesAcademy of Interactive Entertainment Sydney
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MS

Mid-level Java Full-Stack Developer specializing in enterprise web applications

USA4y exp
AccentureWilmington University
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VS

Senior Full-Stack Engineer specializing in AI-driven SaaS platforms

Huntington Beach, CA7y exp
EnerfloRochester Institute of Technology
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SC

Principal Full-Stack Engineer specializing in cloud-native platforms and AI-powered developer tools

Coral Gables, FL12y exp
Kipu HealthGoldey-Beacom College
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VM

Mid-level Full-Stack Developer specializing in FinTech

Saint Charles, MO3y exp
BNY MellonWebster University
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PB

Senior .NET Full-Stack Developer specializing in healthcare and financial services

Pennsylvania, USA8y exp
CEI AmericaRobert Morris University
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HK

Mid Software Engineer specializing in iOS, backend systems, and AI-powered applications

Hyderabad, India4y exp
EntainSan Jose State University
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SC

Principal Full-Stack Engineer specializing in Healthcare IT and FinTech

Remote12y exp
Kipu HealthGoldey-Beacom College
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MA

Senior DevOps/Site Reliability Engineer specializing in multi-cloud Kubernetes platforms

Austin, TX11y exp
KIBO
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DP

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

Columbus, IN6y exp
Cummins
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SK

Executive Enterprise Architect specializing in cloud, ERP/CRM, integrations, and AI/ML

30y exp
Valcourt Group
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BP

Executive Technology Leader specializing in IT transformation, cloud infrastructure, and cybersecurity

20y exp
Digital Monitoring Products
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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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VT

Venkata Tharun Seemakurthi

Screened ReferencesStrong rec.

Mid-level AI Software Engineer specializing in automation, RAG, and data systems

Remote, US3y exp
SwanTechUniversity of Florida

Founding AI engineer at an AI SaaS startup who built the full GTM knowledge and retrieval stack for non-technical teams, driving 60% less manual effort and 25% faster deployments. Also brings enterprise B2B SaaS integration experience from Wipro, including external API/documentation work for large-scale partner ecosystems.

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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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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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GJ

Glen Jarvis

Screened ReferencesStrong rec.

Staff Site Reliability Engineer specializing in cloud infrastructure and automation

Remote, NY17y exp
OS LabsMissouri University of Science and Technology

Infrastructure/automation engineer with experience bridging post-acquisition environments (Pandora + SiriusXM) by building an API-driven integration to provision Debian workloads on RHV while preserving iPXE-based imaging workflows. Strong in deep debugging across virtualization/network/OS layers (e.g., resolving virtio/vCPU contention causing network/NFS issues) and in extending automation tooling via custom Ansible/Python modules. Also has exposure to biomanufacturing on-prem devices (Hamiltons, shakers) alongside AWS microservices.

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WB

Wesley Barta

Screened ReferencesStrong rec.

Senior Software Engineer specializing in full-stack FinTech platforms

Houston, TX18y exp
GSFSGroupUniversity of Houston

Engineering leader with recent hands-on depth across TypeScript/React, Go, and Python who led a 15-person team through a zero-downtime migration from a legacy monolith to a Module Federation architecture. Has B2B SaaS experience in an insurance agent portal, combining security and performance work through RBAC, OAuth 2.0, and edge-based authentication.

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Preethi Bandari - Mid-level Full-Stack Developer specializing in scalable web applications in Memphis, TN

Preethi Bandari

Screened ReferencesStrong rec.

Mid-level Full-Stack Developer specializing in scalable web applications

Memphis, TN5y exp
MetLifeUniversity of Memphis

Developer who uses AI tools pragmatically to accelerate coding while keeping full ownership of system design and decision-making. Emphasizes rigorous review, testing, and alignment with architecture, security, and performance standards, and stays current on AI through both industry sources and hands-on experimentation.

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