No cost, no commitment - we'll make a personal intro
DA
Danish Asim
Mid-Level Full-Stack Engineer specializing in cloud-native e-commerce and AI/ML systems
University of MichiganUniversity of Maryland, College ParkDearborn, MI3 Years ExperienceMid LevelWorks On-Site
Connect with Danish
Danish already has a relationship with Reval, so a warm intro from us gets a much better response than cold outreach.
Typically responds within 24 hours
Recommended
Already have an account?
About
Full-stack engineer with strong ownership in fast-moving environments: designed and shipped a pre-order/campaign inventory system (NestJS + Strapi + Datadog) that freed 34% warehouse space and reduced stock risk to ~5.7%. Also built rapid, high-impact logistics features (Spot Sales) that drove last-mile cost to ~0 in ~40 days, and has hands-on AWS/Terraform/CI-CD experience including deploying a global RAG system with Pinecone, Datadog, and PagerDuty.
Hire with Reval
Find your next great hire
Our AI agents source, screen, and vet candidates for your open roles. Get qualified candidates within 48 hours.
Mid-level AI/ML Engineer specializing in LLMs, NLP, and real-time AI systems
New York, NY4y exp
New York UniversityNYU
“Backend engineer who built a real-time pipeline for recording, transcribing, and analyzing audio from 400+ news radio stations, scaling Whisper on an HPC cluster with 7 H100 GPUs. Has strong performance optimization experience (30% latency reduction via SQL/query design; 50% DB call reduction via Redis caching) and has implemented region-based data isolation and PII protections in a regulated environment (JP Morgan Chase).”
Mid-level Machine Learning Engineer specializing in NLP, LLMs, and multimodal modeling
Ann Arbor, USA3y exp
University of MichiganUniversity of Michigan
“Built and productionized a telecom-focused RAG assistant by LoRA fine-tuning LLaMA-2 and integrating LangChain+FAISS behind a FastAPI service, with dashboards and a human feedback UI for engineers. Demonstrated measurable impact (≈40% faster document lookup, +8–10% retrieval precision) and strong MLOps rigor via Airflow orchestration, CI/CD, and monitoring for drift and failures.”