Vetted Amazon S3 Professionals

“Backend/data engineer with experience at Citi and Elevance Health, building end-to-end pipelines and data services in regulated, high-volume environments. They combine Python, SQL, .NET, Azure Functions, and strong observability/reliability patterns to improve processing speed, reduce manual effort, and maintain high uptime across financial and healthcare data platforms.”

“Backend/AI workflow engineer who built production-grade orchestration systems for hardware security verification at Silicon Assurance (Nextflow/Python/Postgres) and a multi-agent LLM-driven regulatory code checking system at the University of Florida. Emphasizes reliability: strict plan/execute/verify boundaries, queue-based isolation, and strong observability/auditability with Prometheus/Grafana and persisted prompts/tool calls.”

“Full-stack product engineer with hands-on ownership across React/TypeScript, serverless backends, and Postgres, combining technical depth with strong UX instincts. Stands out for measurable impact: improved a slow query from seconds to under 200ms and increased onboarding completion by about 25%, while also building reusable platform primitives and scalable multi-tenant configuration systems.”

“Unity/C# game engineer with about 3 years of experience who owned a high-impact runtime content delivery platform at Zynga/Singa powering LiveOps releases for roughly two years. Their work cut app size by 35-40%, removed a 25K daily activator drop on release days, improved load times by 25%, and supported a feature that drove 10% quarterly revenue growth.”

Pahuldeep Singh

Screened

Senior Full-Stack Developer specializing in scalable web platforms and automation

Remote6y exp

“Backend/full-stack engineer focused on TypeScript/Node.js systems, with hands-on ownership of a real-time telemetry and dashboard platform built on Kafka, Debezium, PostgreSQL, and GraphQL. Stands out for combining event-driven architecture, correctness/idempotency patterns, strong observability, multi-tenant security, and developer-friendly API design in production environments.”

“Salesforce-focused candidate with hands-on experience owning Sales Cloud automation end to end, including requirements translation, Flow/Apex design, testing, and production deployment. They show practical judgment in choosing hybrid declarative/code solutions, bulk-safe Apex patterns, and modern UI work across both LWC and legacy Aura components.”

Chanatip Vachirathanusorn

Screened

Entry-level Software Engineer specializing in full-stack and FinTech systems

San Diego, CA2y exp

“Software engineer with AppFolio payments experience who shipped an end-to-end payment adoption rollout during a short internship, including full-stack implementation, stakeholder coordination, and real-time monitoring. Also built an insurance service AI agent inspired by his father's agency, combining structured document ingestion, RAG, and agentic integrations to improve reliability on dense policy documents.”

Anirban Ghosh

Screened

Mid-level Machine Learning Engineer specializing in data science and cloud systems

Seattle, WA4y exp

“ML engineer who independently pitched and built a recommendation engine at Danske Bank in a legacy fintech environment, creating compliant data pipelines and deployment infrastructure from scratch and delivering a 62% engagement lift with 70%+ advisor adoption. Also worked at AWS on classification and GenAI-powered reporting systems, with strengths spanning production ML, platform setup, monitoring, and research-to-production optimization.”

“ML/LLM systems engineer who has owned AI support automation products end-to-end, including ServiceNow-integrated incident routing, RAG-based resolution suggestion systems, and production stabilization. Stands out for combining hands-on platform work across PySpark, AWS Glue, FastAPI, Kubernetes, and Pinecone with measurable operational impact, including 30-35% MTTR reduction and 25-30% improvement in first-touch resolution.”

Chaitanya Prasad Reddy Narala

Screened

Mid-level AI/ML Engineer specializing in FinTech risk and fraud systems

USA4y exp

“Senior AI/ML engineer focused on production LLM systems, combining RAG, fine-tuning, distributed training, and AI safety to ship scalable real-time moderation and conversational AI platforms. Stands out for pairing deep AWS/Kubernetes MLOps expertise with measurable impact: 40% lower latency/cost, 30-50% fewer hallucinations, and major reliability gains through observability and automation.”

“ML/AI engineer with hands-on experience shipping regulated financial AI systems at JPMC and Capgemini, spanning credit risk, fraud detection, and generative AI assistants. Stands out for combining modern LLM/RAG architectures with strong MLOps, real-time infrastructure, and explainability/compliance practices, while delivering measurable business impact in latency, accuracy, cost, and risk reduction.”

Farhan Shahbaz

Screened

Senior Software Engineer specializing in cloud infrastructure and platform engineering

New York, NY4y exp

“Backend engineer with deep experience in security and access-management platforms at JPMorgan Chase, including owning automation for migrating 50+ engineering teams from CyberArk to HashiCorp Vault. Stands out for combining regulated-environment rigor, infrastructure automation, and production operations with practical AI integration in internal access workflows.”

“Backend/ML-focused engineer with experience spanning fintech, sales enablement, and medtech, including a Capital One capstone and a Singapore medtech startup internship. Stands out for owning end-to-end AI/backend systems, from a GenAI sales pitch platform that cut prep time by 50% to an ultrasound-guidance MVP for non-expert operators in a highly ambiguous domain.”

“Engineer with hands-on experience across frontend, backend, and data systems, including React/TypeScript UI work at CitiGroup, ETL pipeline ownership at Accenture, and personal 0→1 builds like an AI chatbot and a real-time multiplayer typing platform. Stands out for combining product-minded prioritization with strong implementation depth in performance optimization, type-safe frontend architecture, and resilient data pipeline design.”

“AI/ML-focused engineer with practical experience building RAG-based and multi-agent systems, including architectures for retrieval, reasoning, context processing, and response generation. Stands out for combining LLM productivity gains with disciplined software engineering practices like validation, monitoring, and reproducibility.”

Malyadri Peruru

Screened

Senior Software Engineer specializing in backend, cloud, and IoT automation

Greater San Francisco, CA13y exp

“Software engineer with strong systems design experience spanning both core product infrastructure and AI-enabled support workflows. Co-led a from-scratch B2B SaaS billing platform for complex customer pricing models, and also improved an orchestrated agent system by tightening context, metadata, and human-review safeguards for misrouted support tickets.”

Adithi Anugu

Screened

Mid-level Full-Stack Software Engineer specializing in API and data-driven applications

Florida, USA4y exp

“Full-stack engineer with internship experience at Sports Excitement and AI systems work at the University of Florida, combining React/TypeScript, Node.js, and PostgreSQL with applied AI workflow tooling. Stands out for shipping measurable product improvements—30% faster pagination and 40% higher engagement—and for building observability, eval, and human-review layers around AI-assisted data extraction systems.”

“AI/ML-focused software engineer who has built and orchestrated multi-agent systems with separate retrieval, planning, validation, execution, and escalation components. Stands out for combining hands-on experimentation with a strong reliability mindset, using observability, structured logging, tracing, and evaluation to make agentic workflows production-ready.”

Deepthi Pamisetty

Screened

Mid-level Full-Stack Engineer specializing in FinTech and cloud-native systems

Dallas, TX6y exp

“Full-stack engineer with production experience building AI-powered search and automation systems at JPMorgan Chase and customer-facing product features at Wayfair. Stands out for combining React frontend work with backend microservices, RAG/LangChain AI integration, and cloud-scale performance tuning, including a support chatbot that reduced ticket resolution time by 35%.”

“IBM full-stack engineer focused on document automation for internal and government use cases, building Java Spring Boot services and React/TypeScript UIs that automate XML-to-multilingual PDF workflows. Stands out for turning complex backend automation into usable tools for non-technical operations teams while improving processing speed and reducing manual effort.”

“Built and shipped AI-powered fraud investigation and risk dashboard features at Barclays, combining full-stack engineering with RAG-based LLM systems in a compliance-sensitive environment. Stands out for pairing explainable AI architecture with measurable business impact: improving fraud detection accuracy by 18% while reducing analyst investigation time by 14%.”

“Full-stack engineer with hands-on ownership of both a marketplace discovery product and a zero-to-one AI document intelligence assistant. Brings a strong blend of React/Node/MongoDB product engineering and production RAG/LLM system design, with clear experience turning technically complex AI capabilities into usable experiences for non-technical users.”

“Biopharma R&D product leader with 6-8 years building enterprise data, analytics, and AI products for drug discovery and biologics research. Notably led a multi-site biosample discovery platform that became an enterprise-wide capability at Bristol Myers Squibb and also drove an AI-powered scientific insight platform using RAG/LLM approaches to reduce time-to-insight for researchers.”