Vetted Distributed Tracing Professionals

“Built and deployed a production LLM-powered text-to-SQL/document intelligence chatbot on AWS that lets non-technical business users query complex enterprise databases in plain English. Demonstrates deep practical expertise in schema-aware prompting, embeddings-based schema retrieval, SQL safety/validation guardrails, and rigorous offline/online evaluation with human-in-the-loop approvals for risky queries.”

“Backend engineer focused on real-time, event-driven distributed systems (Node.js/TypeScript) with strict latency and reliability requirements. Deep hands-on experience debugging concurrency issues and designing resilient workflows (idempotency, circuit breakers, compensating actions) with strong observability; familiar with ROS/ROS2 concepts and confident ramping into robotics integrations.”

“Built and deployed a production RAG-based clinical decision support assistant at MedLib, focused on fast, trustworthy answers from large medical documents. Demonstrates deep practical experience improving retrieval accuracy (semantic chunking + metadata-aware search), controlling hallucinations with grounded generation and thresholds, and adding clinician-requested citations using chunk metadata, with evaluation driven by healthcare professional review.”

“Backend/AI engineer who built production-grade intelligence systems in high-stakes domains including tax/legal document analysis and brain tumor MRI workflows. Stands out for combining LLM/RAG product delivery with strong engineering rigor around retrieval evaluation, grounding, validation, observability, and safe fallbacks—turning impressive demos into systems users could actually trust.”

Sriram Krishna

Screened

Mid-Level Software Engineer specializing in AI/ML and cloud-native platforms

Redmond, WA5y exp

“Backend/AI engineer who has built production LLM orchestration and agentic workflow systems in Python/FastAPI on Kubernetes across AWS/Azure. Demonstrated strong reliability engineering by debugging a real-world memory retention issue that caused latency spikes/timeouts, and strong data/performance chops with a PostgreSQL optimization that cut query latency from ~1.2s to ~15ms. Targets roles building scalable, guardrailed AI-driven workflow automation with robust observability and human-in-the-loop controls.”

Samee Rauf

Screened

Junior AI/Full-Stack Software Engineer specializing in ad automation and LLM systems

Dallas, Texas3y exp

“Full-stack engineer with deep ad-tech/marketing automation experience, building production tools that reduce programmatic ad waste and improve search ads performance. Shipped and operated AWS-deployed, Dockerized systems with Postgres/Redis and strong observability (Datadog/OpenTelemetry), and delivered measurable impact (25k campaigns processed, 50k sites negated, 3–4 hours/week saved). Built scalable abstractions for multi-platform ad integrations, enabling rapid onboarding of additional clients.”

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 and owned a production-grade RAG/LLM support automation system on AWS using GPT-4, Pinecone, FastAPI, and Redis, taking it from initial experimentation through deployment, monitoring, and iterative improvement. Their work reduced support workload and ticket volume by about 40%, improved CSAT and self-service resolution, and they also created shared Python/LLM infrastructure that accelerated other teams' delivery from weeks to days.”

“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.”

“Built and shipped a production LLM-powered incident response agent for a microservices platform, automating alert triage and safe remediation recommendations with strong guardrails (RAG grounding, structured JSON outputs, rule-based validation, and human-in-the-loop). Implemented state-machine orchestration (Redis/Kafka), comprehensive eval/monitoring, and an error categorization pipeline that cut hallucination errors ~40% and reduced MTTR ~30%.”