Pre-screened and vetted.
Director-level Engineering Leader specializing in scalable cloud platforms and real-time AI systems
Senior Java Developer specializing in cloud-native microservices and event-driven systems
Senior Machine Learning Engineer specializing in Generative AI and MLOps
Mid-level Front-end Engineer specializing in SaaS, AI, and Healthcare IT
Senior Full-Stack Engineer specializing in Python back-end systems and scalable web apps
Staff/Lead DevOps & Site Reliability Engineer specializing in cloud infrastructure and Kubernetes
Senior Full-Stack Engineer specializing in Python, React, and cloud-native AI features
Mid-level Data Scientist specializing in NLP, RAG, and information retrieval for RegTech
“Built and deployed a production document Q&A/research platform that combines semantic search (vector DB embeddings) with structured knowledge-graph querying to reduce analyst research time. Used in high-stakes domains like Politically Exposed Person profiling and extracting critical information from ESG/regulatory documents, with a human-in-the-loop evaluation process (precision@k and source-text highlighting) to ensure accuracy.”
Senior Full-Stack & Machine Learning Engineer specializing in scalable SaaS and cloud AI
“Frontend engineer who led an enterprise self-serve analytics dashboard end-to-end using a micro-frontend React/TypeScript architecture with strong integration discipline (contracts, CI gates, ADRs). Demonstrated measurable performance wins (35% faster LCP) through code splitting, lazy loading, and tighter Redux subscriptions, and uses feature flags plus automated E2E coverage for controlled rollouts.”
Junior AI/ML Engineer specializing in Python ML, NLP, and model deployment
“Built and productionized a real-time social-media sentiment analysis system used by a marketing team to monitor brand/campaign performance. Experienced in orchestrating LLM workflows with LangChain (validation → prompting → parsing → post-processing), plus monitoring, retraining, and RAG-style retrieval using embeddings/vector stores to keep outputs reliable over time.”
Mid-level AI/ML Engineer specializing in anomaly detection, data tooling, and cloud-native systems
“Backend/platform engineer who built an LLM-driven QA automation system (“mockmouse”) using a Flask orchestration microservice, Socket.IO real-time updates, Redis caching, and strict Pydantic schemas to turn prompts into reliable action graphs and automated browser tests. Has hands-on Kubernetes delivery experience (Docker/Helm/Jenkins) and has supported large migration programs, validating ETL cutovers with 1M+ synthetic records and rigorous output comparisons; also built event-driven monitoring/anomaly detection streaming into Grafana.”
Mid-Level Full-Stack Software Engineer specializing in web platforms, cloud, and test automation
“Full-stack engineer with hands-on ownership of production systems, including a Kafka-based notification/alerting platform (Node.js + React) deployed on AWS with Docker/GitHub Actions, achieving ~95% email delivery reliability. Demonstrates strong operational maturity (observability, CI/CD, zero-downtime migrations) and experience shipping in ambiguous environments (SJSU project) with evolving requirements.”
Intern Software Engineer specializing in AI, cloud, and backend systems
“Candidate has internship and graduate-project experience building AI agents, including a production log-analysis assistant using a lightweight agentic/RAG-style workflow with local GPT training and validation against historical logs. They also worked on Android/iOS game build and release processes in a Unity-based robot racing game environment, and highlight measurable LLM outcomes including 80% analysis accuracy, 2-5 second latency, and 50% cost reduction.”
Junior Software Engineer specializing in AI, full-stack development, and applied ML
“AI/full-stack product builder who has shipped production agentic systems in both customer support analytics and medical claims automation. They combine React/Next.js frontends with Python-based async backends and LLM orchestration, delivering measurable outcomes like 60% cost savings, 40% less manual review, and reducing claims processing from 30 minutes to 20 seconds.”
Mid-level Full-Stack & Cloud Engineer specializing in backend, AWS infrastructure, and DevOps
“IBM Power/AIX engineer who has owned a large production estate (20+ Power9/Power10 frames and 400+ LPARs) with vHMC and dual-VIOS HA. Has hands-on incident recovery experience (NPIV/RMC issues, LPM restores) and PowerHA failovers, plus modern DevOps exposure using Terraform on AWS and CI/CD with GitHub Actions/Jenkins (including deploying AI/RAG and vision workloads).”
Junior Software Engineer specializing in automation and full-stack development
“Backend-focused engineer who built a time-sensitive data retrieval system for a source with no public API, using an AWS EC2-hosted persistent browser session plus a PostgreSQL TTL caching layer—cutting manual retrieval by 99% and achieving sub-10-second average retrieval. Emphasizes production security (Secrets Manager, encryption, IP allowlisting, rate limiting) and robustness via testing and edge-case handling (atomic file operations).”
Mid-level Frontend/Full-Stack Engineer specializing in React and scalable web apps
“Frontend-focused engineer who leads end-to-end delivery of high-performance React + TypeScript products, including legal-tech client platforms and a large-scale case management dashboard handling thousands of records. Strong in SEO for SPAs, strict code quality automation, and performance work (Lighthouse 95+, 40% FCP reduction), plus disciplined rollout practices using LaunchDarkly, canary releases, and Sentry monitoring.”
Mid-level Data Engineer specializing in cloud ETL and big data pipelines
“Data engineer focused on building reliable, production-grade pipelines and data services end-to-end, including a 50+ GB/day pipeline ingesting from APIs/files into Snowflake with PySpark/SQL transformations. Emphasizes strong data quality controls, monitoring/retries, and performance optimization, and has also shipped a Python data API with caching and backward-compatible versioning.”
Junior Full-Stack Software Developer specializing in React, Node.js, and AWS
“Frontend engineer at WITT who led multiple end-to-end React/TypeScript products in fintech/e-commerce contexts, including a shopping cart with Stripe payments and a multi-step registration flow. Emphasizes scalable component architecture, strong QA (tests/reviews/linting), and performance work (lazy loading/memoization), plus disciplined rollout via feature flags and close product/design collaboration.”
Mid-level Product Designer & Design Technologist specializing in design systems and GenAI UX
“Enterprise/industrial UX designer focused on making complex, real-time automated systems feel trustworthy and predictable. Has hands-on experience observing operators in logistics/automation environments, building shared interaction models to unify fragmented products, and collaborating tightly with engineers using component-system thinking (HTML/CSS/TypeScript) to ship resilient UIs that handle partial failures.”
Mid-level Full-Stack Software Engineer specializing in FinTech and real-time systems
“Full-stack product engineer with a strong real-time systems focus: built and rolled out a WebSocket-based notifications system (with robust reconnect/resync and event ordering protections) that cut update latency to under 200ms. Also owned a workflow automation platform backend in FastAPI (JWT/RBAC, versioned APIs, standardized errors), designed the PostgreSQL schema for workflows/tasks/executions, and operated deployments on AWS ECS Fargate with blue-green CI/CD and performance stabilization via caching and autoscaling.”
Intern Full-Stack Engineer specializing in AI-powered SaaS products
“Solo builder of OGym, shipping production AI features for gyms that turn member behavior/health data (workouts, attendance, nutrition, payments, device metrics) into prioritized, actionable owner and member insights. Designed and implemented FastAPI backends, PostgreSQL-based RAG workflows, guardrails (RBAC/validation/rate limiting), and real-user evaluation loops, with a strong focus on latency/cost optimization and reliable data pipelines.”
Junior AI Full-Stack Engineer specializing in LLM automations and RAG systems
“Built and shipped a production LLM-powered customer support assistant using a Python/FastAPI backend with RAG (embeddings + vector search) over internal docs and product/operational data. Instrumented the system with logging/metrics and ran continuous eval loops; post-launch improvements focused on retrieval quality (chunking/ranking) and performance/cost tradeoffs (query classification, caching, validation guardrails).”