Vetted Amazon RDS Professionals

Siddharth Ranjan

Screened

Mid AI/ML Engineer specializing in LLMs, RAG, and cloud AI systems

Maryland, USA4y exp

“Built an AI-powered job matching platform end to end using AWS, Gemini, FastAPI, TypeScript, embeddings, and vector search. The standout result was automating manual matching workflows and scaling resume processing to roughly 2,000 resumes per minute while monitoring quality with F1 score and latency metrics.”

“Built a real-time AI meeting assistant using a Chrome extension that streams audio to a backend LLM workflow with transcription and RAG, then hardened it for production with queue-based streaming, async pipelines, security controls, and full observability. Also has hands-on startup sales experience, partnering with customers to define measurable technical win conditions (latency/accuracy) to close deals and drive adoption.”

“LLM engineer/data analyst who built a production RAG QA assistant over the Jurafsky & Martin NLP textbook to reduce hallucinations and provide explainable, source-grounded answers. Experienced with LangChain/LangGraph orchestration, retrieval optimization (embeddings, vector DBs, caching), and rigorous evaluation/monitoring (Retrieval@K, A/B tests, telemetry/drift). Previously communicated analytics insights to non-technical stakeholders at GS Analytics using Power BI and simplified reporting.”

“Frontend engineer who has led customer-facing React + TypeScript products end-to-end, building complex dashboards with robust async state patterns (caching, deduping, cancellation, optimistic updates) and strong quality practices (TypeScript standards, layered testing, production monitoring). Experienced modernizing inherited codebases through modularization and performance work (code splitting/memoization) while aligning stakeholders and shipping safely via feature flags and staged rollouts.”

“Full-stack engineer from Clairvoyant who led end-to-end delivery of a cloud-native, event-driven platform: Spring Boot microservices + Kafka real-time streams with an Angular UI, migrated and containerized on AWS, and automated CI/CD with Jenkins/Maven/Git. Demonstrates depth in distributed consistency challenges (partitioning, consumer lag/duplicates) and database performance tuning across SQL/NoSQL under heavy workloads.”

“Startup-style full-stack/backend engineer with hands-on AWS architecture experience who shipped an LLM-driven assessment-question automation feature (Python microservice calling AWS Bedrock via SQS, deployed on Lambda) with strong validation/guardrails and retry strategies. Also improved production scalability by moving a CPU/IO-heavy file upload path out of a Go API into a queue/Lambda design monitored with CloudWatch, and has React+TypeScript experience optimizing analytics dashboards.”

“Full-stack engineer operating in early-stage, high-velocity environments (OpGov.AI/UST Calibrate) who ships production Next.js App Router features end-to-end (RSC, Server Actions, SEO, RBAC, caching) and owns performance post-launch. Demonstrates strong data/infra depth—designed Postgres JSONB-based event models for DevOps/DORA analytics and tuned queries from ~2s to <50ms, plus built durable ingestion workflows with retries and idempotency on Azure.”

“Built and shipped production AI search and RAG features for a university portal, including an embeddings-based semantic search layer and a documentation-grounded assistant with citations and anti-hallucination prompting. Also developed scalable, reliable data pipelines integrating Google Ads/GA4/Meta APIs for automated reporting, with strong focus on evaluation loops and retrieval quality improvements (hybrid search, chunking, query-log driven iteration).”

Sanskar Pandey

Screened

Junior Full-Stack Java Developer specializing in FinTech microservices

OH, USA3y exp

“Full-stack engineer with production experience building a real-time order tracking system using React + Firebase/Firestore, emphasizing audit-friendly data modeling, state-machine-based status transitions, and strong post-launch ownership (performance, security rules, reliability). Demonstrated measurable frontend performance gains by isolating real-time updates to dynamic components and applying memoization, plus backend reliability patterns (idempotency, retries) and SQL query/index optimization validated with EXPLAIN ANALYZE.”

“Built DocMind, an end-to-end PDF chat assistant using React/TypeScript, FastAPI, and Postgres/pgvector, showing full-stack ownership plus practical performance tuning and AWS debugging skills. At Social Tech Labs, improved onboarding, shipped lean under ambiguity, and created a reusable low-latency feature serving layer that reduced duplicated infrastructure work across models.”

“Early major technical hire who helped build fintech startup Ugami from MVP to near Series A, while supporting bridge round and Series A fundraising with technical materials for leadership. Also grew from intern to lead engineer at a venture-backed product shop, giving him firsthand exposure to investor expectations, startup incubation, and practical AI opportunities with tight scope and strong unit economics.”

“Candidate is actively using AI-assisted development tools, including MCP server integrations with Copilot, to generate boilerplate test scripts, validate code standards, and handle package updates. They also have hands-on experience choosing different agents based on task requirements and serving as an admin for AI tool access.”

“Software engineer with a strong builder mindset who has worked across ML, backend, and frontend systems. Notably built an AI-driven predictive autoscaler for Kubernetes from scratch using Prometheus, TensorFlow, Flask, and Spring Boot, and also delivered customer-facing automation features in financial document processing by working directly with auditors to translate domain rules into product logic.”

Furqan Munir

Screened

Mid-level Full-Stack MERN Engineer specializing in AI-integrated web platforms

Lahore, Pakistan4y exp

“Early-stage startup engineer who operated with little structure, worked directly with clients, and helped turn Deloitte and Informatica into permanent clients. More recently, they built an AI-enabled HVAC project bidding and estimation platform using FastAPI, LangChain, React, TypeScript, and PostgreSQL, including prompt tuning and UAT-driven improvements.”

SAUD SALEEM

Screened

Senior Python Engineer specializing in scalable backend and distributed systems

Stockton, CA6y exp

“Senior Software Engineer who has operated in a functional implementation capacity on B2B SaaS projects, especially identity verification and infrastructure intelligence platforms. Stands out for translating stakeholder and compliance requirements into APIs, workflow logic, data mappings, and UAT processes, with experience supporting utility-focused asset risk and maintenance decision platforms at scale.”

“Software/product engineer who owns customer-facing internal platforms end-to-end, with deep experience building data pipeline health and data quality tooling (near-real-time alerting and ops dashboards). Strong in React/TypeScript + Python REST architectures and microservices with RabbitMQ, emphasizing reliability patterns (idempotency, DLQs, correlation IDs) and fast, safe iteration via feature flags, testing, and observability.”

“Software engineer/intern who built an EV charging station management platform from scratch (TypeScript/Next.js/Node/Express/Postgres) with real-time OCPP WebSocket operations and payment processing, iterating quickly based on operator feedback. Also created an internal CloudWatch log aggregation dashboard with Slack alerts that was adopted team-wide, addressing API rate limits and log-format inconsistencies through caching, pagination, and standardized parsing.”

“Backend engineer who owned the API layer for an AI trust/analytics dashboard (trust scores, stability checks, public verification endpoints) using Python/FastAPI and Postgres. Has hands-on DevOps experience deploying FastAPI and Node.js services to AWS Kubernetes with GitHub Actions + ArgoCD GitOps, plus Kafka-based real-time event streaming and careful staged migration practices (shadow traffic/dual writes, rollback planning).”

“Built and deployed a production generative-AI copilot at Tungsten that automates invoice/form extraction template creation, reducing weeks of manual model-building work. Combines fine-tuned LLMs (PyTorch/HuggingFace) with OpenCV layout grounding to reduce hallucinations, and runs an end-to-end Kubeflow-based MLOps pipeline with drift monitoring, canary releases, and automated retraining.”

“Game QA professional with AAA open-world shipping experience, owning high-impact quality risks like save-data corruption, progression blockers, performance drops, and multiplayer desync. Demonstrates strong systems thinking and exploit prevention (e.g., reproduced and helped fix a reward-duplication race condition using network interruption + rapid input), with disciplined JIRA/TestRail workflows and evidence-driven bug reporting.”

“Cloud/DevOps-focused engineer with hands-on experience building Azure DevOps CI/CD pipelines for containerized applications deployed to AKS, including security scanning, approvals, versioned artifacts, and rollback. Also implemented Terraform-based IaC for Azure (VNets/subnets/NSGs/AKS) with modular design, remote state/locking, and drift detection; resolved a real deployment outage caused by an Azure RBAC permission change.”

“Software engineer with experience across edtech, live gaming, and an AI document intelligence platform, delivering end-to-end customer-facing features and production backends. Built secure, automated live-session scheduling integrating Zoom and TalentLMS (JWT/RBAC, idempotency, transactions) cutting setup time from ~3 minutes to under 1 minute, and optimized real-time gaming dashboards/APIs with query tuning, caching, and CDN improvements (~60% latency reduction under peak load) on AWS.”

“Product-minded engineer who has owned features end-to-end, including a full onboarding redesign that lifted completion ~25% and a production LLM/RAG report-generation system with strong guardrails (schema-constrained JSON, confidence gating, logging) and an automated eval/regression loop built from real user queries. Also built a scalable research data pipeline ingesting messy PDFs/JSON/CSVs with normalization, idempotent reruns, observability, and cost/latency tradeoffs.”