Vetted Unit Testing Professionals

“Founding engineer who has built web products end-to-end in startup settings, spanning FastAPI/React application development, auth, cloud deployment, and Kubernetes-based scaling. Particularly notable for designing custom GPU autoscaling for an AI-style recommendation product and later shipping workflow-driven healthcare support tooling using Temporal, Postgres, and modular backend logic.”

“Built and shipped an end-to-end autonomous telemetry and log-triage product that combined LLM-based anomaly analysis, strict typed validation, and a React observability UI. Particularly compelling is their focus on making non-deterministic AI reliable in production at scale—500,000 daily requests and 99.9% uptime—while also translating complex AI output into a usable experience for non-technical teams during live outages.”

“Engineering leader with hands-on AI/ML systems experience spanning production inference infrastructure and consumer-facing LLM products. At Jio, they led a 17-person AI features team and delivered measurable execution gains, including 40% faster deployments and 35% lower prediction latency, while also building an end-to-end RAG-based meal recommendation product using OpenAI and Gemini.”

Zack Pagano

Screened

Junior Full-Stack Software Engineer specializing in FinTech and data platforms

San Francisco, CA3y exp

“Engineer in a highly regulated banking environment who has built both a full-stack digest product that cut notification volume 40% and an internal LLM-powered on-call assistant that reduced triage time by 30%+. Stands out for combining strong AWS/serverless and TypeScript architecture skills with careful AI safety, observability, and compliance-minded design.”

“Backend-leaning full-stack engineer with PayPal experience building payment orchestration, settlement, and merchant risk systems at production scale. Stands out for combining cloud-native AWS delivery, database/query performance tuning, and reliability work in event-driven microservices, including a monolith-to-microservices migration that doubled deployment frequency and cut incident response time by 40%.”

Akhila Gadige

Screened

Mid-level Full-Stack Developer specializing in Python, Java, cloud, and GenAI

Remote4y exp

“Backend-leaning full-stack engineer who has built and operated high-throughput GPU telemetry and task/event platforms using React, FastAPI, PostgreSQL, Redis, Celery, and Kubernetes. Stands out for hands-on ownership of production performance at scale, including partitioning 100M+ row tables, fixing timeout bottlenecks, and delivering measurable gains like 3x throughput and 95% fewer timeout errors.”

“Backend/platform engineer who has built and run production Python/Flask + Kafka microservices processing RFID and camera/RFID fusion streams for near-real-time retail cart updates at ~4–5M events/day. Strong in reliability/performance debugging (p99 latency, Kafka lag, Cosmos DB RU hot partitions) with measurable impact including ~30% database cost reduction, and has also shipped an end-to-end vulnerability scanning workflow with DynamoDB-backed state, idempotency, and robust retry/verification guardrails.”

“Robotics software engineer with hands-on GPU/CUDA vision work (solo-built a 4-fisheye panorama stitcher using camera intrinsics/extrinsics) and mapping/localization expertise, including radar-driven pose-graph mapping optimized with Ceres. Strong ROS background (Cartographer, AMCL, TEB) and demonstrated localization improvements by biasing AMCL with Cartographer to reduce drift; experience shipping modules deployed across large robot/vehicle fleets (e.g., retail scanning robots and automotive).”

“Built and deployed a production LLM/RAG clinical decision support system that enables real-time semantic search over unstructured EHR notes and delivers patient risk insights. Strong in healthcare-grade MLOps and compliance (HIPAA, PHI handling, encryption, RBAC, audit logs) and scaled embedding/retrieval pipelines using Spark/Databricks and Airflow. Partnered with clinicians via Power BI dashboards and explainability, contributing to an 18% reduction in patient readmissions.”

“Interned at Zonda where he built an AI-driven semantic search solution over ~280M housing/builder records. Iterated from local LLMs via llama.cpp quantization to a vector-embedding retrieval system, then boosted semantic accuracy with a custom spaCy NER layer and re-ranking, optimizing for latency through precomputation. Collaborated with economics-focused stakeholders to reduce manual document/paperwork time by enabling natural-language search over internal data.”

“JavaScript engineer with hands-on experience improving performance in data-heavy table UIs (thousands of rows), including an open-source DataTables extension fix that reduced redundant AJAX calls via debouncing and was merged upstream. Comfortable profiling/benchmarking, optimizing DOM and network behavior, and collaborating with OSS maintainers through GitHub issues/PRs while also producing clear developer documentation and quick-start examples.”

“Full-stack engineer with primary depth in .NET Core and Python who has built and deployed end-to-end AWS applications (Lambda, API Gateway, S3, CloudFront) and supported them in production. Experienced in scaling large, data-driven workloads using queues/background workers, batching, and database tuning, with strong focus on API contracts, observability, and resilience patterns; also has hands-on React/TypeScript and some Spring Boot exposure.”

“Software developer with healthcare operations experience at Epic Systems (Referrals & Authorizations), delivering customer-facing tooling to speed manual insurance authorization/denial documentation and support future automation. Also supported an HRIS migration to Workday at Aloe Yoga, solving legacy ID interoperability via scripting and mapping, and demonstrates strong production debugging and test-driven maintainability practices.”

“PhD-trained engineer with ~3.5 years of consulting experience building simulation/ML-driven manufacturing software. Deployed an ML surrogate model as a .NET C# DLL integrated with MES workflows, and resolved a critical pre-production latency issue by redesigning serialization/storage. Also built Python-based integrations across CAD/CAE tools and cloud material databases using an XML data model, with a strong interest in digital twins and real-to-sim/sim-to-real robotics workflows.”

“Full-stack engineer with 3+ years using Java/Spring Boot (Citi) and React, who built a production observability dashboard monitoring 53 microservices across 17 clusters with real-time health/latency tracing and significant performance improvements (cut load time from ~10s). Also designed a serverless AWS face-recognition system (Lambda/S3/SQS) built to handle burst traffic (~1000 concurrent requests), demonstrating strength in scalable, event-driven architectures.”

“Engineering leader/CTO-type with deep experience building and scaling a vehicle routing platform at Transdev On Demand, including a nationwide rollout to 22 US airports ahead of schedule. Drove engineering best practices (CI/CD, high test coverage, pair programming, automated deployments) and led a multi-tenant architectural upgrade to expand the routing engine to additional business lines and external customers.”

“Backend-focused engineer who built a Python/Flask task-management API with JWT/RBAC, modular service/repository architecture, and PostgreSQL/SQLAlchemy performance optimizations (indexes, lazy loading, bulk ops, pooling). Also implemented multi-tenant data isolation strategies and built an OpenAI-powered document summarization workflow using chunking, async processing, Redis background workers, and caching to improve throughput.”

“McKinsey data/ML practitioner who led production deployment of an entity resolution + semantic search platform for unstructured finance and healthcare data, integrating with legacy systems under HIPAA constraints. Deep hands-on stack across transformers (spaCy/HF BERT), embeddings + FAISS, and production MLOps/workflow tooling (Airflow, Docker, CI/CD, Prometheus/Grafana), with reported gains of +30% decision speed and +25% search relevance.”

“Data/ML practitioner with experience applying NLP and classical ML to large-scale customer data (2B+ records) for segmentation, prediction, and survey-text classification, delivering measurable business impact (~18% engagement efficiency). Has hands-on entity resolution across multi-source datasets and has built embedding-based semantic search using SentenceBERT + a vector database with domain fine-tuning (~20% relevance improvement), plus production workflow experience with Spark/Airflow and cloud tooling (AWS/Azure).”

“AWS Solutions Architect intern who advised customers on securing a multi-tenant LLM-based SaaS, including isolation strategy tradeoffs and production guardrails against prompt injection. Has experience investigating a prompt-injection incident using logs/traces and TTP-style documentation, and designing scalable SDK/agent integrations via asynchronous worker architecture with prompt versioning.”

“Data engineer/platform builder with experience across PwC and Liberty Mutual delivering high-volume, production-grade pipelines and real-time data services. Has owned end-to-end streaming + batch architectures on AWS and Azure, including web scraping systems, with quantified reliability gains (99.9% availability, 90%+ error reduction, 30% latency reduction) and strong observability/CI-CD practices.”

“Unreal Engine UI engineer focused on scalable, production-ready UI architecture (C++/Slate/UMG/CommonUI) with strong designer enablement via decoupled, interface-driven patterns and MVVM. Demonstrated measurable performance wins: replaced 200+ per-frame Blueprint bindings to cut UI prepass/paint from 4.2ms to 0.5ms and reduced VRAM by ~120MB using texture streaming proxies.”

“Full-stack engineer with production experience building a security integrations portal in Next.js (App Router + TypeScript), using server components and typed route handlers as a secure proxy to multiple third-party security vendors. Demonstrated ability to scale performance significantly (server-side re-architecture for 1M+ datapoint dashboard filtering; Postgres query tuning from 1–2s to <200ms) and to own features post-launch (reliability, caching/background sync, and rapid onboarding of new integrations) in a pre-Series B through post-Series C environment.”

“ML/Backend engineer with ServiceNow experience building production-grade inference services on FastAPI with Docker/Kubernetes (autoscaling, health checks) and strong reliability practices (monitoring, retries/timeouts, fallbacks). Delivered measurable improvements including 30% lower API latency and 18% higher model accuracy, and built A/B testing plus drift-triggered retraining loops to keep models stable in production.”