Pre-screened and vetted.
Senior Full-Stack Engineer specializing in LLM apps and RAG-based knowledge retrieval
Senior Unix/Linux Systems Administrator specializing in Linux, AIX/Solaris, and HA clusters
Mid-Level Full-Stack Software Engineer specializing in cloud platforms and security
Mid-Level Software Engineer & Consultant specializing in enterprise platforms
Senior QA Engineer specializing in test automation and end-to-end software testing
Junior Full-Stack Software Engineer specializing in cloud-native healthcare applications
Mid-level Full-Stack & AI Engineer specializing in FinTech and ML-powered applications
Senior Software Engineer specializing in Python backend, microservices, and cloud/DevOps
Mid-Level Software Engineer specializing in backend, cloud, and data pipelines
Junior Full-Stack Software Engineer specializing in cloud-native web apps and APIs
Mid-Level Software Engineer specializing in ML and Generative AI applications
Senior Full-Stack & AI Engineer specializing in FinTech and Healthcare
Senior Software QA Engineer specializing in test automation and CI/CD
Mid-Level Software Engineer specializing in AI/ML, cloud deployment, and full-stack systems
Mid-Level Full-Stack Software Developer specializing in modern web apps
“Product-focused full-stack builder who has shipped and operated multiple production apps from scratch, including an e-commerce bakery delivery scheduler (with concurrency controls and timezone handling) and a real-time passenger music-request system for Lyft rides that hit and resolved YouTube API rate-limit scaling issues via debouncing and caching. Strong in React+TypeScript and Node.js/TypeScript backends, with solid PostgreSQL/PostGIS data modeling and performance tuning.”
Junior AI/ML Engineer specializing in LLM automation and NLP
“Built and shipped a production LLM hallucination detection and monitoring pipeline using semantic-level entropy (embedding-clustered multi-generation variance) to flag unreliable outputs in downstream automation. Implemented a scalable async architecture (FastAPI + Docker + Redis/Celery) with strong observability (structured logs + PostgreSQL) and developed evaluation loops combining controlled prompts and human review; also partnered with non-technical stakeholders on AI-driven form validation/document processing.”
Mid-level AI Engineer specializing in LLM fine-tuning, RAG, and agentic systems
“Building and deploying production in-house, domain-specific LLM chatbots for enterprises that cannot use third-party GPT tools due to internal policies. Focused on reducing latency and improving domain awareness using fine-tuning, continual learning, and advanced RAG/agent retrieval strategies, with experience orchestrating multi-agent workflows via LangChain/LlamaIndex and vector DBs (FAISS, Weaviate, Chroma).”
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).”
Mid-level XR Developer specializing in Unity VR/MR training simulations
“XR/VR Unity developer who has shipped to Meta Quest and led industrial VR projects. Built a Meta Quest 3 scaffolding safety training simulation using the XR Interaction framework, emphasizing instructional UX (cognitive load reduction) with interactive audio guidance, and has hands-on experience debugging device-only URP rendering issues (e.g., anti-aliasing conflicts causing ghosting/distortion).”
Mid-Level Full-Stack Software Engineer specializing in AI agents and cloud platforms
“Backend/data engineer focused on climate/emissions data platforms, building production Python (FastAPI) microservices and AWS serverless/ETL pipelines (Glue/Athena/Lambda/EventBridge). Demonstrated strong reliability and observability practices plus measurable optimization wins, including cutting PostgreSQL query runtimes from minutes to seconds and reducing AWS costs from ~$6k/month to ~$400/month.”