Vetted Azure Blob Storage Professionals

Pre-screened and vetted.

AK

Mid-level Data Engineer specializing in cloud data pipelines and analytics platforms

Chicago, IL6y exp
American ExpressLewis University
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MD

Senior .NET Full-Stack Engineer specializing in Healthcare and Financial Services

Minnetonka, MN14y exp
UnitedHealth GroupUniversity of North Carolina
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BP

Mid-level Full-Stack Engineer specializing in FinTech and enterprise platforms

Arizona, USA5y exp
DeloitteArizona State University
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AG

Senior Full-Stack & AI/ML Engineer specializing in cloud-native platforms and LLM systems

OR, US11y exp
NikeUniversity of Illinois Urbana-Champaign
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RR

Mid-Level Software Engineer specializing in full-stack systems and cloud platforms

New York, NY4y exp
Goldman SachsUniversity at Buffalo
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HM

Mid-level AI/ML Engineer specializing in LLM and production ML systems

6y exp
eBayLamar University
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DF

Principal DevOps/SRE Engineer specializing in multi-cloud infrastructure and DevSecOps

Houston, TX12y exp
ComcastUniversity of Texas at Austin
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VG

Mid-level Full-Stack Engineer specializing in cloud-native microservices and data integrations

5y exp
Johnson & JohnsonPurdue University
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CM

Senior Software Engineer/Tech Lead specializing in healthcare platforms and microservices

Orlando, FL9y exp
Orlando HealthFlorida International University
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YO

Senior AI Platform Engineer specializing in agentic AI and RAG systems

Alpharetta, GA7y exp
Morgan StanleyKakatiya Institute of Technology and Science
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LW

Senior Software Engineer specializing in data platforms and FinTech/SaaS systems

Boston, MA13y exp
KlaviyoUniversity of Connecticut
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SB

Senior Software Engineer specializing in full-stack web development

San Francisco, CA9y exp
Assort HealthGeorgia Tech
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KR

Mid-level Software Developer specializing in backend cloud and API platforms

California, USA3y exp
UberNew York Institute of Technology
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OO

Senior DevOps/DevSecOps Engineer specializing in AWS & Azure cloud infrastructure

Fairfax, VA10y exp
Technatomy Digital SolutionUniversity of Lagos

Infrastructure/DevOps-focused engineer working across Linux-based enterprise platforms that include IBM Power/AIX in a broader OpenShift/Kubernetes and cloud ecosystem. Built Azure DevOps CI/CD for containerized deployments and resolved a production deployment failure by tracing ImagePullBackOff to outdated registry credentials in Kubernetes secrets. Uses Terraform (with modular structure) plus Ansible to provision and standardize production environments with pipeline-based validation.

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CK

Mid-level Data Engineer specializing in cloud data platforms and FinTech analytics

Chicago, IL4y exp
IntuitDePaul University

Solutions architect/technical consultant with experience across Intuit, Deloitte, and CodeNest Solutions, focused on enterprise data modernization, AI adoption, and real-time streaming in B2B environments. Particularly strong in regulated financial use cases, where they combine hands-on POC building, security/compliance diligence, and modern data stack expertise to help clients modernize legacy systems and close complex enterprise deals.

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VV

vishal varma

Screened

Mid-level Generative AI Engineer specializing in LLMs, RAG, and MLOps

6y exp
CVS HealthUniversity of Bridgeport

Built and deployed a production RAG-based LLM Q&A and summarization platform for internal documents, emphasizing grounded answers with structured prompting and citations to reduce hallucinations. Experienced orchestrating end-to-end LLM workflows with LangChain plus cloud pipelines (Azure ML Pipelines, AWS), and runs iterative evaluation using both metrics (accuracy/hallucination/latency/cost) and real user feedback to drive reliability.

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DK

David Kidwell

Screened

Senior AI/ML Data Scientist specializing in NLP, computer vision, and MLOps

New York, NY10y exp
Canoe IntelligenceBinghamton University

Applied LLMs and a graph-RAG architecture in Neo4j to automate an accounting firm's cross-checking of transactional books against tax regulations, indexing 1,000+ pages into a knowledge graph with vector search. Combines agentic LLM workflows with classical NER (Hugging Face/NLTK) and validates using expert-labeled held-out data plus precision/recall and measured accountant time savings after deployment.

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