Pre-screened and vetted.
Mid-level Software Engineer specializing in Python, distributed systems, and AI backend services
Senior Machine Learning Engineer specializing in LLM inference and GPU infrastructure
Senior Software Engineer specializing in cloud-native microservices and observability
Director-level Software Development Manager specializing in AWS infrastructure and distributed systems
Mid-level Software Engineer specializing in systems, CUDA, and robotics/AI
Staff Software Engineer specializing in FinTech and scalable distributed systems
Mid-level AI/ML Engineer specializing in LLM training, RAG, and low-latency inference
Executive technology leader specializing in cloud platforms, AI, and enterprise architecture
Director-level Software Development Manager specializing in large-scale cloud platforms
Intern Software Engineer specializing in distributed systems and cloud infrastructure
“Built and operated a production warehouse metadata collection platform at Sigma Computing, integrating Go/gRPC services with a TypeScript backend and MySQL, with strong emphasis on idempotency, retries, bounded-concurrency job queues, and Datadog-based observability. Also created Kurral (kurral.com), an AI agent runtime security and observability/governance SDK/proxy concept, iterating via pilot-customer feedback and market research; targeting founding engineer roles with $180–200k base and ~2–5% equity.”
Mid-Level Software Engineer specializing in cloud infrastructure and full-stack web development
“Backend engineer at Electric Hydrogen who built a serverless device-log ingestion and processing platform in Python/Flask, scaling throughput (4x peak ingestion) while keeping sub-300ms API latency. Strong in Postgres/SQLAlchemy performance (partitioning, materialized views) and production ML integration (ONNX model served via FastAPI microservice with async batch inference, Redis feature caching, and drift monitoring via S3/Lambda). Experienced designing secure multi-tenant systems with schema-per-tenant isolation and KMS-backed encryption.”
Senior Software Engineer specializing in FinTech and distributed systems
“Backend/AI engineer who has built a rule-service platform on AWS and evolved it into an agentic RAG system using LangChain, ReAct, tool calling, and LLM-as-judge review. Notable for combining heavy AI-assisted development with production safeguards like manual CR, CloudWatch monitoring, fallback strategies, benchmark testing, and user-feedback-driven model improvement.”
Executive Technology Leader (CTO/VP Engineering) specializing in enterprise AI and data platforms
“Freelance CTO with a track record of founding and building early web products, including an award-winning baby website creation platform that was acquired by eStyle after gaining traction via retail/mail-order distribution and PR. Also built a sports event text-alert product, engineering around missing data APIs via a replaceable scraping layer.”
Junior Software Engineer specializing in backend systems and AI/ML pipelines
“Robotics-focused engineer with ROS 2 experience who has built and debugged real-time, distributed control/orchestration systems under production-like latency and safety constraints. Led platform changes at Persona for a real-time verification orchestration system using deterministic state machines and async workers, and has hands-on experience stabilizing multi-robot navigation/SLAM behavior using rosbag, RViz, and stress testing in simulation (Gazebo).”
Engineering Manager specializing in AI/ML platforms and 0→1 product delivery
“Player-coach engineer/lead on a high-scale research integrity platform ("Lighthouse") that flags fraud/manipulation signals across ~3M academic manuscripts per year. Owns architecture decisions (ADRs), implements across Go/Java/React services, and introduced NLP (SciBERT embeddings + human-in-the-loop) to assess out-of-context citations while also handling production incidents with a data-consistency-first approach.”
Mid-Level Full-Stack Software Engineer specializing in distributed systems and cloud-native microservices
“Backend engineer (4 years) who built an end-to-end Python backend for a patent-pending in-car massager/heater system, including GraphQL data modeling and Bluetooth integration with an ESP32 microcontroller (reverse engineered a niche protocol). Also has strong platform experience: on-prem Kubernetes/CI-CD (Jenkins/GitLab, exploring ArgoCD GitOps), Terraform-based infra workflows, a RabbitMQ messaging library used across microservices, and an on-prem migration of ~30 critical applications with rollback/parallel-run strategy.”
“Backend/full-stack engineer (Amazon experience) who built an AWS-based integration testing platform using Flask, ECS, Docker, and CloudWatch—cutting 1000+ test cases from ~5 hours to ~30 minutes while improving log visibility for non-engineering users. Also led a zero-downtime EU region migration with rigorous ORR testing, and built a Kinesis/Firehose/S3 + Glue/Spark replay mechanism for resilient data recovery. Side project: reproducible, cost-efficient LLM hosting platform on EKS using CDK and Karpenter for scale-to-zero.”
Intern Software Engineer specializing in AI, cloud-native systems, and MLOps
“Backend/full-stack engineer who has owned a production recruiting platform end-to-end (TypeScript/Node microservices for scraping/cleaning/serving job data, RabbitMQ for spike handling, MongoDB + Elasticsearch, AWS containers) with pragmatic CI, logging/alerts, and Docker Compose E2E tests. Also operated high-traffic event pipelines during a Binance internship using Kafka + Redis idempotency, with strong observability and failure-mode/rollback/degradation practices, and has experience designing developer-friendly REST APIs and resilient browser automation for E2E flows.”
Principal Cloud & Digital Transformation Architect specializing in Financial Services and Data Platforms
“Technology-first venture builder with strong familiarity in the VC/accelerator landscape, specializing in greenfield innovation, M&A, and large-scale transformation/modernization. Described building a venture-funded retail banking greenfield startup to integrate lending-as-a-service for SME lending while meeting federal and local financial services compliance requirements.”
Mid-level Software Engineer specializing in AI/LLM and distributed systems
“Recent internship project at Google Workspace building an LLM-driven Python backend pipeline to extract/enrich NLP features from messy customer web domains and integrate them into a Domain Feature Store for personalization and promotions. Also has hands-on Kubernetes/Docker deployment experience for a Digital Signage SaaS backend with GitHub Actions CI, plus strong streaming-systems knowledge (Kafka exactly-once, schema evolution, Flink scaling) and built an information retrieval system handling 30,000+ cases.”
Intern Software Engineer specializing in backend and distributed systems
“Backend engineer with experience at ByteDance (TikTok monetization) and Baidu, plus a personal real-time course booking/tracking platform built with FastAPI, Postgres, and Redis. Demonstrates strong concurrency and reliability engineering (Redis distributed locks with TTL extension, idempotent event processing) and practical DevOps skills (Kubernetes/Helm, GitLab CI/CD, Docker build-time optimization).”
Senior Backend Engineer specializing in distributed microservices and event-driven systems
“Backend engineer with production experience building a high-scale notification pipeline (~20M/day) using Java/Dropwizard with Kafka and Azure Queue, including DLQ/poison-message handling and the outbox pattern for reliability. Also led a batch-based migration of Yammer Messaging user data from PostgreSQL to Azure Cosmos DB for global multi-region scale, addressing throttling and network failures via retries, escalation policies, and dynamic throughput tuning.”
Mid-level Software Engineer specializing in distributed backend systems on AWS
“Built production systems in the AWS ecosystem, including an internal AI assistant for diagnosing account transfer and permissions issues and an end-to-end account transfer workflow used by enterprise customers. Stands out for combining LLM/RAG design with strong distributed systems reliability practices, emphasizing guardrails, fallbacks, and operational trust in high-stakes workflows.”