Vetted Data Structures and Algorithms Professionals

Garv Goyal

Screened

Intern software engineer specializing in AI systems and full-stack development

Vienna, VA1y exp

“Full-stack/product-minded engineer with a strong infrastructure foundation who has built both cloud automation systems and an AI voice interview coach. Stands out for combining hands-on coding with pragmatic product thinking: they improved user trust through scorecard redesign, built resilient speech handling around flaky browser APIs, and delivered measurable backend performance gains during startup internships.”

“Analytics-oriented candidate with hands-on GTM and sales operations experience in financial services plus applied project leadership at Northeastern. Built reporting systems in Power BI/Tableau, used Salesforce for client segmentation and campaign tracking, and created reusable launch-management tools adopted by multiple teams.”

“Full-stack engineer with healthcare/AI platform experience (Humana), owning an end-to-end high-risk patient prediction feature from React dashboards through FastAPI/TensorFlow real-time inference to AWS EKS operations. Emphasizes production reliability and contract-driven APIs (OpenAPI + generated TS types), plus strong data integration patterns (Kafka, idempotency, DLQs, backfills) in regulated, high-traffic environments.”

“AI/LLM engineer with production experience building an LLM-powered investment recommendation system using RAG and chatbots, deployed via Docker/CI/CD and scaled on Kubernetes. Demonstrated measurable performance wins (sub-200ms latency) through QLoRA fine-tuning and TensorRT INT8/INT4 quantization, plus strong MLOps/orchestration background (Airflow ETL + scoring, MLflow monitoring) and stakeholder-facing delivery using demos and Tableau dashboards.”

“Backend/full-stack engineer who has owned production TypeScript systems in both fintech-style transaction/rewards flows and HIPAA-regulated healthcare platforms. Deep focus on correctness and reliability (idempotency, retries/DLQs, reconciliation, observability) plus strong infra automation (Docker/Terraform/CI-CD) and measurable performance wins (40% query improvement, 90% test coverage).”

Mohammed Mudassir Hussain Shaikh

Screened

Entry-Level Full-Stack Software Engineer specializing in serverless AWS and AI applications

Tempe, AZ1y exp

“Built and deployed serverless AWS applications (Lambda/S3/RDS Proxy) including a NASA L’Space React + Python data analysis tool, focusing on performance for large datasets. Demonstrates strong cloud troubleshooting across compute and networking (CloudWatch-driven diagnosis, EC2 scaling, security group fixes) and a user-driven iteration loop that improved product usability with dynamic filtering and interactive UI.”

Yash Prakashbhai Shah

Screened

Senior Backend Software Engineer specializing in Java, microservices, and cloud infrastructure

Santa Clara, CA6y exp

“Backend/platform engineer at Aryaka Networks who built a centralized resiliency and security Spring Boot library to standardize Keycloak RBAC and fault-tolerance across 25+ Kubernetes-migrated microservices. Uses profiling and observability (Prometheus/Grafana) to drive measurable performance and reliability gains (25% faster APIs, 70% faster environment setup) and accelerates adoption via golden-path starter repos and Swagger/OpenAPI live docs.”

Ruslan Labza

Screened

Mid-level Full-Stack Engineer specializing in modern web applications

San Francisco, CA6y exp

“Built and launched a production AI chat assistant inside a data processing platform, focused on helping users understand large table outputs and job results faster. Brings strong end-to-end product engineering across React/TypeScript frontend, backend APIs, and LLM integration, with a clear emphasis on reliability, safe behavior, and iterative quality improvements after launch.”

“Built and shipped AI products at Humanitarians AI, including a full-stack multi-agent platform that consolidated six faculty AI tools into one interface and achieved 100+ user adoption, 70% less workflow switching, and a 6x latency improvement. Also designed a grounded document parser using FAISS and structured LLM outputs that reduced hallucinations by 60%, showing strong depth in both product-minded engineering and production AI systems.”

“Front-end/full-stack engineer with about two years of experience building high-stakes internal trading operations tools for traders and operations engineers. Stands out for combining deep browser performance work, strong TypeScript/React architecture, and direct user observation to improve incident response workflows in live production environments.”

“Full-stack and AI systems engineer who has built across ride-hailing, fintech, higher-ed support, and legal-tech workflows. Stands out for shipping production RAG/agent systems with careful grounding and human fallback, while also delivering hard backend architecture wins like geospatial dispatch scaling and cutting fintech payment latency from 60 seconds to 2 seconds.”

“Candidate worked on an AI-powered commercial real estate document analysis product at Ariesview, building workflows that ingest leases and property reports, run OCR, persist structured outputs, and support grounded Q&A via RAG. They stand out for practical ownership of messy production problems like noisy PDFs, document-state consistency, and backward-compatible schema migrations in a fast-moving product environment.”

“Developed and productionized VL-Mate, a vision-language, LLM-powered assistant aimed at helping visually impaired users understand their surroundings and query internal knowledge. Emphasizes reliability and safety via confidence thresholds, uncertainty-aware fallbacks, hallucination grounding checks, and rigorous offline + user-in-the-loop evaluation, with experience orchestrating multi-step LLM pipelines (LangChain-style and custom Python async) and deploying on containerized infrastructure.”

“Built a production lead-generation system using AI agents that researches the internet for relevant leads and integrates RAG-based contact enrichment/shortlisting aligned to existing CRM data, enabling sales reps to focus more on selling. Also has hands-on AWS data orchestration experience (Glue, Step Functions) moving raw data into Redshift and evaluates agent performance with human-in-the-loop plus BLEU/perplexity metrics.”

Jyothsna V

Screened

Mid-level Backend Software Engineer specializing in Python/FastAPI and cloud-native microservices

USA4y exp

“Backend engineer who evolved Coca-Cola bottlers' Trade Promotion Optimization platform at Coke One North America, building domain-focused microservices in Node.js and Python (Flask/FastAPI) with PostgreSQL. Experienced in multi-tenant security (OAuth2/JWT, RBAC, row-level scoping by bottler/region), API contract/versioning discipline, and Azure DevOps-driven incremental rollouts with strong observability.”

“Builder of practical AI-backed products across developer tooling, travel search, defense, and healthcare-style workflows. They shipped an MCP/FastAPI/Gemma context-compaction system that cut token usage by about 80%, built a flight-price AI layer that validates LLM output against live search data, and helped shape a visionOS command center for a military air wing.”

“Full-stack and AI systems engineer who has shipped both enterprise fintech and real-time voice AI products. At NCR, they repaired a broken transaction-fee workflow end to end using React/TypeScript, Lambda, and DynamoDB; at Lillup, they turned a research voice prototype into production infrastructure, cutting latency from 350ms to under 200ms and raising user ratings from 2.8 to 4.1. They also built an LLM side project that uses Claude and GPT to cross-review each other through structured JSON consensus rounds.”

“Frontend product builder who has shipped and maintained a two-mobile-app ecosystem (user + employee) backed by Node.js, emphasizing separation of concerns, shared libraries for reuse, and TypeScript type safety. Re-architected a Sunmor Research codebase using MVC, improving readability and collaboration and taking the product from unusable to working, with a strong regression-testing mindset and customer-feedback-driven iteration.”

Aneri Patel

Screened

Junior Machine Learning Engineer specializing in LLM fine-tuning and semantic retrieval

Washington, D.C.2y exp

“Backend engineer with legal-tech and AI workflow experience: built JurisAI, an end-to-end legal research system using OCR + embeddings + Pinecone vector search to deliver citation-grounded LLM answers with safe failure modes (~90% recall@K). Also led a GW Law metadata migration into Caspio with batch validation and parallel rollout, and has strong FastAPI/GCP production reliability and observability practices.”

Prithvi Jai Ramesh

Screened

Junior Robotics Engineer specializing in AI, perception, and autonomous navigation

Tempe, AZ1y exp

“Robotics software engineer with 2+ years of ROS/ROS2 experience who built a mobile robot stack from scratch (Fusion 360 → URDF → ROS) and integrated teleop, SLAM, and navigation. Worked in an ASU lab applying deep learning for person tracking on a TurtleBot setup, and solved real deployment issues like Raspberry Pi video-stream latency via compression and on-board processing. Also reports experience with CI/CD tooling (Jenkins) and Kubernetes.”

Tamanna Nandlal Choithani

Screened

Entry-level Full-Stack Engineer specializing in AI and distributed systems

California, USA1y exp

“Full-stack engineer who built an AI-based inventory/procurement query system at Botlily/Botlerly using Flask and Google Sheets as a live knowledge base, overcoming Sheets latency with caching and structured in-memory models. Demonstrated strong LLM product engineering (40% accuracy improvement via preprocessing/prompting) and customer-driven iteration with bar/restaurant owners, evolving the tool into a more comprehensive inventory management and forecasting solution.”

Gowhith Gandem

Screened

Intern Software Engineer specializing in distributed systems and backend engineering

Remote, USA1y exp

“Frontend engineer with hands-on experience building real-time collaborative and visualization-heavy React applications, including a document editor using WebSockets, Redis, and PostgreSQL. Demonstrates strong performance instincts, with a concrete example of reducing dashboard load time from 3.2s to 2.1s at MegaViz while improving responsiveness for streaming data interfaces.”