Pre-screened and vetted.
Senior Software Engineer specializing in event-driven microservices and GenAI for payments
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic AI
Staff-level AI/ML Engineer specializing in enterprise RAG, agentic automation, and AI governance
Senior Data Scientist specializing in ML, fraud risk, and Generative AI (RAG/LLMs)
Mid-level AI/ML Engineer specializing in GenAI, computer vision, and real-time ML pipelines
Senior Full-Stack AI/ML Engineer specializing in personalization, NLP, and GenAI platforms
Senior Security Engineer specializing in AWS cloud security and DevSecOps
Senior ETL/Data Engineer specializing in cloud data platforms and AI/ML-ready pipelines
Mid-level Data Scientist / ML Engineer specializing in LLMs and predictive analytics
Senior Data Engineer specializing in cloud data platforms and real-time streaming pipelines
Senior Full-Stack Engineer specializing in cloud-native Java microservices and GenAI
Senior Data Scientist specializing in healthcare analytics and scalable ML pipelines
Mid-Level Software Engineer specializing in distributed systems and GenAI
“Capgemini engineer with 4+ years building and deploying high-availability, low-latency fraud detection APIs and multi-cluster distributed systems for a Fortune 20 bank, including zero-downtime production rollouts and multi-layer (SQL/network/hardware) performance debugging. Also built a Python + OpenAI/LangChain LLM-powered grading workflow for Austin School for Women, cutting feedback time from 90 minutes to 5 minutes per submission for 200+ learners.”
Engineering Manager specializing in enterprise SaaS, cloud architecture, and AI/ML
“Senior engineering manager who stays hands-on (~50/50) while leading teams through design reviews, code reviews, and production issue triage. Shipped scalable platform features (notifications, user action tracking via microservices) with strong quality/performance practices (TDD, high-load testing). Owned a complex cross-system SSO/IdP incident end-to-end, identifying a SameSite+iFrame root cause and delivering a configurable product fix plus support documentation.”
Mid-level Product Owner / Application Developer specializing in supply chain ERP and agentic AI platforms
“Architect/product owner/lead developer who built high-scale ERP supply chain and inventory transaction capabilities (including order-to-order pegging) with strong performance tuning in Postgres and robust monitoring/reprocessing dashboards. Also led product for an enterprise agentic development platform using LLM integrations to generate user stories, data models, workflows, and RBAC-secured applications, with sandboxing and promotion guardrails plus UAT across technical and non-technical personas.”
Mid-Level Software Engineer specializing in geospatial AI and cloud security automation
“Cloud engineer and cloud OS SME (Chevron) who productionized large-scale security remediation—using Tanium and Ansible to address CIS benchmark noncompliance across 5,000+ servers with robust logging and RCA handoffs. Also drives adoption of a geospatial AI refinery inspection product by consolidating siloed imagery into an enterprise geospatial database, and presents internally on agentic/LLM tooling (LangChain/LangGraph, LangSmith observability).”
Senior Full-Stack & AI Engineer specializing in LLM integrations and cloud-native systems
“Backend/data engineer with hands-on production experience building FastAPI Python APIs and AWS-native platforms (Lambda/API Gateway, SQS, ECS Fargate) with Terraform + GitHub Actions CI/CD and strong reliability practices (JWT/RBAC, retries/timeouts, structured errors/logging). Also built AWS Glue ETL pipelines (S3/RDS to curated S3/Athena) with schema evolution and data quality controls, modernized legacy processing via parallel-run validation and phased cutovers, and has demonstrated SQL tuning impact (seconds to <200ms) plus incident ownership for batch pipeline SLAs.”
Mid-level AI Engineer specializing in GenAI agents and RAG for IT operations
“Built and operates a production LLM agent for enterprise IT operations that triages and drafts resolutions for high-volume ServiceNow tickets using LangChain + RAG (Pinecone/pgvector) and AWS Bedrock/OpenAI. Emphasizes reliability with schema-validated stages, offline eval datasets from real tickets, and CloudWatch-driven monitoring/guardrails; system scales to 40K+ tickets/month and cut resolution time ~28%.”