Vetted Flask Professionals

Pre-screened and vetted.

GG

Mid-level Business Intelligence Engineer specializing in AI-powered analytics

New York, NY3y exp
LTIMindtreeWright State University
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MC

Senior Data Scientist specializing in ML engineering and cloud analytics

Calgary, Canada8y exp
Geek Inc.Georgia Tech
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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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SV

Mid-level Software Engineer specializing in full-stack, cloud, and AI systems

Bloomington, IN4y exp
Indiana UniversityIndiana University
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JZ

Mid-level Software Engineer specializing in FinTech and crypto systems

St. Louis, MO5y exp
Sylvanus TechnologiesLaunch School
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JM

Senior Software Engineer specializing in distributed systems and full-stack development

Hayden, ID24y exp
AmdocsFullerton College
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KC

Senior AI/ML Engineer specializing in MLOps and Generative AI (LLMs/RAG)

Chicago, IL10y exp
United Airlines
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VS

Mid-level Applied AI Engineer specializing in Generative AI and RAG systems

Dallas, Texas5y exp
AT&T
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PM

Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics

Westlake, OH4y exp
KeyBank
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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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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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Likitha Kukunarapu - Mid-level Applied AI Engineer specializing in data engineering and healthcare AI in Remote, USA

Likitha Kukunarapu

Screened References

Mid-level Applied AI Engineer specializing in data engineering and healthcare AI

Remote, USA3y exp
Community Dreams NGONortheastern University

Built production LLM agents spanning document Q&A, financial insight generation, and ERP-like operational data workflows, with a strong focus on reliability, grounding, and evaluation. Stands out for translating LLM systems into measurable business outcomes, including 70%–80% support workload reduction and a fallback-rate improvement from 18% to 8% through targeted RAG iteration.

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SM

Sai Manikanta Kasireddy

Screened ReferencesStrong rec.

Mid-level Machine Learning Engineer specializing in cloud-native GenAI and RAG systems

5y exp
Revstar ConsultingUniversity of North Texas

Built and productionized an internal GenAI chatbot that makes company policy/SOP knowledge instantly searchable, using a secure RAG architecture on AWS (Bedrock/Titan embeddings/OpenSearch Serverless, Textract/Lambda/S3 ingestion, Claude 3 Sonnet). Demonstrates strong MLOps/orchestration experience (Airflow, Step Functions with Lambda/Glue/SageMaker) and a rigorous reliability approach (RAGAS metrics, A/B testing, citation validation, monitoring), including collaboration with compliance stakeholders via review dashboards.

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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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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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RD

Rohitha Dollu

Screened ReferencesStrong rec.

Entry-level Software Engineer specializing in backend, cloud, and data systems

Remote1y exp
KneadNortheastern University

Built across cloud infrastructure, AI-powered product workflows, and backend data reliability in environments including Northeastern, Knead, and Grafx. Particularly compelling for roles needing someone who can both ship AWS-based systems end-to-end and debug messy production issues involving caching, APIs, and data pipelines.

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KI

Khuram Ismaeel

Screened ReferencesModerate rec.

Senior AI/ML Engineer specializing in machine learning and cloud-native AI systems

10y exp
SoftServeAir University

ML/AI engineer with hands-on ownership of production recommendation and GenAI systems, spanning experimentation, deployment, monitoring, and iteration. Stands out for delivering measurable outcomes—22% CTR lift, 15% conversion lift, and a 30% reduction in support tickets—while demonstrating strong judgment on latency, cost, and safety tradeoffs in real-world systems.

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