Pre-screened and vetted.
Senior Software Engineer specializing in full-stack, data engineering, and AI/ML
Senior Full-Stack Engineer specializing in React, Next.js, and Healthcare IT
Senior DevSecOps & Observability Engineer specializing in cloud automation and STIG compliance
Mid-level Backend Software Engineer specializing in scalable systems and healthcare workflows
Executive CIO/CTO specializing in healthcare IT, privacy compliance, and enterprise application delivery
Executive Founder/CEO specializing in AI platforms for healthcare and enterprise automation
Mid-level Full-Stack Software Engineer specializing in cloud-native and AI-driven applications
Executive IT & Software Development Leader specializing in cloud-native transformation and trading platforms
Executive Healthcare Technology Leader (CIO/CTO) specializing in digital transformation
Mid-level Full-Stack Developer specializing in cloud-native FinTech and Healthcare IT
Executive product leader specializing in enterprise platforms across healthcare and FinTech
Senior Software Engineer specializing in AI-powered backend and data platforms
Principal Engineering Leader specializing in AI platforms and game backend systems
Executive technology leader specializing in AI, cloud, and healthcare transformation
Executive Engineering Leader specializing in cloud, DevSecOps, and large-scale platform modernization
“Co-founded a Digital Loss Prevention (DLP) startup and raised $6M in seed funding by showcasing a controlled, laptop-based technology demo. Post-funding, drove MVP planning and execution by sequencing operations and assembling a team to build an appliance MVP, using an iterative build/evaluate/visualize approach.”
Mid-Level Software Engineer specializing in cloud-native systems, automation, and LLM-enabled robotics
“React-focused engineer who built a full-stack analytics/test-metrics dashboard (React frontend + Python backend) and turned common UI pieces (data tables, filter panels, chart wrappers) into a reusable internal component library with docs, examples, and basic tests. Strong on profiling-driven performance optimization (React Profiler, memoization) and on owning ambiguous internal-tool projects end-to-end; now planning to package internal patterns into public open-source components.”
Senior Data Engineer specializing in cloud ETL and real-time streaming pipelines
“Data engineer with eBay experience owning end-to-end pipelines for real-time order and user behavior analytics at 10M+ records/day. Strong in PySpark/SQL transformations, Airflow reliability patterns, and production observability (CloudWatch), with measurable outcomes including improved data quality and 30–40% query performance gains. Also built Python data APIs for analytics/ML consumers with versioning and backward compatibility.”
Senior Data Scientist / ML Engineer specializing in GenAI, LLMs, and NLP
“ML/NLP engineer focused on production GenAI and data linking systems: built a large-scale RAG pipeline over millions of support docs using LangChain/Pinecone and added a LangGraph-based validation layer to cut hallucinations ~40%. Also built scalable PySpark entity resolution (95%+ accuracy) and fine-tuned Sentence-BERT embeddings with contrastive learning for ~30% relevance lift, with strong CI/CD and observability practices (OpenTelemetry, Prometheus/Grafana).”
Staff/Lead Data Scientist specializing in Generative AI, NLP/LLMs, and MLOps
“Lead Data Scientist (10+ years) with recent work in healthcare data: built production pipelines that unify EHR, genomics, and clinical notes using NLP (spaCy/BERT/BioBERT) and scalable Spark-based processing. Also led development of domain-specific LLM/NLP systems for chatbots and semantic search, deploying models via FastAPI/Flask and improving retrieval with FAISS-backed, fine-tuned clinical embeddings and RAG-style workflows.”
Mid-Level Data Engineer specializing in cloud data platforms and streaming analytics
“Data engineer (Intuit) who owned an end-to-end telemetry and subscription analytics platform processing ~22M events/day, built on Kinesis/S3/Glue/Spark/Airflow/Redshift. Strong focus on reliability and data quality (schema drift controls, quarantine layers, idempotent reruns) and performance tuning, achieving a reporting latency reduction from ~15 minutes to under 4 minutes while enabling revenue and churn analytics for business teams.”