Vetted Logging Professionals

Akram Ali

Screened

Senior Full-Stack Developer specializing in Node.js/TypeScript, cloud, and data engineering

Chicago, United States10y exp

“Frontend/fullstack lead who inherited a messy psychological app with production issues, drove a rapid stabilization (2–3 weeks) and major performance/architecture overhaul (Redux Toolkit, memoization, caching, lazy loading, CDN offload to S3/CloudFront). Also owns delivery and infrastructure practices (multi-env, Docker, GitHub Actions CI/CD, AWS ECS + load balancing) and led a 1-week POC for an AI-powered trucking management system (app.neblo.ai).”

Sahilsingh Harcharansingh Khalsa

Screened

Junior Full-Stack/Product Engineer specializing in Next.js, TypeScript, and AWS backends

Seattle, WA2y exp

“Full-stack engineer with startup-style end-to-end ownership, recently shipping a production dashboard at Find Me LLC using Next.js App Router/TypeScript with Supabase + Azure Blob Storage for secure asset/document uploads. Strong server-first React performance mindset and hands-on Postgres modeling/query optimization (EXPLAIN ANALYZE), plus experience building resilient AWS event-driven workflows with idempotency, retries, and DLQs.”

“Unity gameplay engineer who has shipped mobile and Meta Quest VR titles, leveraging DOTS/ECS for large-scale war/rts-style scenes and mobile performance. Built multiplayer features using Photon PUN 2 with PlayFab matchmaking/backend and implemented live network debugging/recovery strategies. Also prototyped an AI/LLM-assisted pipeline for generating and validating gameplay/level content via structured JSON with fallbacks and caching to control reliability and cost.”

“Built an end-to-end agentic population health strategy copilot for healthcare leadership, turning broad chronic disease questions into structured, evidence-backed strategy briefs. Stands out for combining healthcare domain knowledge with production-grade GenAI implementation, including LangGraph orchestration, Databricks/MLflow deployment, human review, and quality gates focused on citations, metrics, risks, and safety.”

Trace Hernandez

Screened

Mid-level Software Automation Engineer specializing in AI and healthcare IT

Santa Rosa, CA5y exp

“Built production automation systems spanning Python integrations, analytics sync pipelines, and internal workflow apps. Notably automated an architectural site inspection process end-to-end with an app, database, and letter generation engine that reduced workload by 20%, and improved reliability of a Botpress/Supabase/Airtable cron integration by eliminating recurring rate-limit failures through batching.”

“New grad software engineer who has already built both a full-stack location-based social app and an internal AI on-call copilot using OpenAI and LangChain. Stands out for combining end-to-end product execution with practical LLM engineering, including RAG, fallback design, citations, and production evals, plus shipping a hackathon-winning MVP in 24 hours.”

“Software engineering intern who built and deployed a full-stack telemedicine platform (React/Node/MongoDB) used daily in a pediatric clinic, incorporating PyTorch-based predictive features. Demonstrated strong customer-facing iteration and production performance debugging—resolved a live slowdown by indexing/optimizing MongoDB queries and adding caching, improving response times by ~50%.”

“Built an AI Image Editor end to end with a React frontend and Flask/PyTorch backend, focusing on the hard operational problems of deploying generative AI reliably across CUDA, Apple MPS, and CPU environments. Particularly strong in backend systems, API design, model lifecycle management, and architectural refactoring that improves reliability and future development speed.”

“Backend engineer (Encore) who led the evaluation and redesign of a high-volume, low-latency real-time retrieval/ranking and inference platform on AWS, shifting from tightly coupled services to a modular architecture for better fault isolation and independent scaling. Strong focus on production reliability, observability, and security (JWT/RBAC, multi-tenant scoping, Postgres/Supabase RLS), with disciplined migration playbooks (feature flags, shadow traffic, dual writes/reconciliation).”

“Built and owned a customer-facing Document Intelligence Service for legal contract analytics at Noasis Digital, delivering extraction/summarization with careful accuracy controls (confidence thresholds, versioned deployments, production logging). Also developed a React/TypeScript document review app and internal QA dashboard, and has hands-on microservices experience with async messaging (RabbitMQ), timeout tuning, and centralized structured logging for reliability at scale.”

“Frontend-leaning engineer who shipped an end-to-end map-based discovery feature in a React Native mobile app, integrating location-based REST APIs with strong UX polish (loading/empty/error states) and cross-platform performance fixes. Also has experience building a Python backend with JWT auth and layered service structure, plus prior infrastructure work setting up centralized logging and monitoring.”

“Built and productionized an AI-assisted merchant onboarding automation workflow for Kort Payments, replacing slow manual underwriting document review with structured extraction, cross-document validation, and human-in-the-loop guardrails. Emphasizes reliability via scenario-based testing, repeatability checks, and deep observability (timestamped logs), plus incremental rollout with legacy fallback to prevent regressions.”

“Built and shipped production LLM agents for real-time, high-concurrency conversational systems, including a RAG-based pipeline with dynamic multi-provider routing and failover that achieved 99.99% reliability and sub-800ms latency. Also architected a UAV telemetry chatbot with tool-calling (anomaly detection/summarization), strict schema validation, and robust eval/monitoring loops, cutting tool-call errors by 30% and reducing operational costs by 90%.”

Ahmad Alomari

Screened

Senior Data Scientist & Machine Learning Engineer specializing in computer vision and production ML

Cleveland, OH7y exp

“PhD in computer engineering who has built production-oriented ML/NLP systems for space-weather prediction using Spark-based ETL on noisy satellite sensor logs. Strong in entity resolution and semantic search—fine-tuned E5 embeddings with contrastive learning and deployed to Pinecone, improving top-5 retrieval precision by 25%—and emphasizes scalable, observable pipelines with Airflow, Docker, and CI/CD.”

Tim Kwok

Screened

Senior DevOps Engineer specializing in multi-region AWS/GCP cloud infrastructure

United States8y exp

“Backend/data engineer with strong AWS production experience spanning FastAPI microservices and large-scale data pipelines. Has delivered containerized Python services on EKS with Terraform/Helm/GitHub Actions, implemented robust auth/secrets practices, and owned ETL reliability (Glue/S3/Redshift) including incident response and idempotent reruns. Demonstrated SQL tuning on 50M-record ETL workloads to remove SLA misses and improve reliability.”

ROHITH DAMARLA

Screened

Mid-level Backend Engineer specializing in high-scale systems and LLM pipelines

Syracuse, NY6y exp

“Open-source-focused TypeScript/JavaScript engineer who built a lightweight Node.js utility library to standardize LLM-agent message formatting, tool invocation, and safe schema-validated JSON outputs. Emphasizes composable abstractions, real-world performance profiling/benchmarks, and strong community feedback loops (GitHub issues, structured errors, logging hooks). Also did research at Syracuse University on converting natural language into structured JSON with validation layers.”

“Analytics professional focused on procurement, supplier, and contract spend analytics, with hands-on experience using Snowflake SQL, Python, and Power BI to turn messy financial and vendor data into decision-ready reporting. They appear strongest in building reusable data workflows, defining business KPIs with finance/procurement partners, and validating results through reconciliation and stakeholder review.”

“Analytics professional with hands-on experience building SQL and Python workflows that turn messy multi-system data into reliable reporting. They’ve delivered measurable impact through automation and customer retention analytics, including cutting month-end reporting from 5 days to 1 day and supporting a churn reduction of about 12%.”

“AI/ML engineer with 9+ years of experience building production recommendation and LLM systems end-to-end, from experimentation through deployment, monitoring, and retraining. Stands out for combining strong MLOps discipline with practical GenAI/RAG implementation, including measurable impact such as ~25% higher engagement on an e-commerce recommender and nearly 30% faster knowledge retrieval from internal documents.”

“Full-stack engineer who built WordCon, an AI-powered vocabulary learning platform, end-to-end across React/Next.js, Python, AWS, and GenAI services. Particularly strong at turning ambiguous AI product ideas into structured, scalable systems by combining deterministic learning logic with LLM-powered personalization, and has additional experience modernizing legacy PHP systems into React/Node architectures.”

“Full-stack and AI product engineer with strong accessibility and voice-interface experience in senior living, building systems for older adults where trust and usability are critical. Has shipped React/TypeScript and .NET MAUI products, productionized Azure OpenAI features, built RAG-based research tools, and improved both product outcomes and technical performance with measurable impact.”

“Cloud infrastructure/DevOps engineer focused on AWS/Azure production environments, with hands-on experience running EKS-based platforms, Terraform-driven IaC, and secure Jenkins/GitLab CI pipelines. Has led real-world migrations from EC2 to Kubernetes using blue/green cutovers and executed multi-AZ failover testing with documented same-day recovery and no data loss; does not have direct IBM Power/AIX ownership experience.”

“Infrastructure engineer focused on AWS platform automation: built a full CloudFormation-based environment (networking, Aurora, ElastiCache) and created Python tooling plus Lambda-backed custom resources to automate deployments, user provisioning (MySQL/RabbitMQ), and operational workflows. Also migrated a company from Rackspace to AWS and maintains an 8-year Debian homelab with VMs/containers, RAID6 backups, and GPU passthrough troubleshooting. No Kubernetes experience.”