Pre-screened and vetted.
Mid-level analytics professional specializing in AI, strategy, and business intelligence
“Analytics-focused candidate with hands-on experience using SQL and Python to clean messy business data, automate reporting, and build practical customer analytics solutions. Notable examples include a 70% reduction in reporting time through Python-based Excel automation at Shell and stakeholder-friendly retention/RFM segmentation work for small business clients in freight and winery contexts.”
Entry Software Engineer specializing in AI/ML and multimodal systems
“Built and shipped a production healthcare AI platform for a clinic in Brea, LA that combined LLM-based clinical report generation, voice agents for appointment workflows, and camera-based patient monitoring. Stands out for pairing multimodal AI architecture with production-grade reliability and compliance practices, while delivering concrete business results including 90% workflow automation, 200 hours saved per month, and a 60% improvement in customer retention.”
Entry-level Computer Science graduate specializing in software and engineering
“Backend engineer focused on high-throughput Python/Flask systems on AWS, with strong scaling and performance tuning experience (e.g., PostgreSQL join reduced from ~3s to <200ms; background aggregation cut from 10 minutes to <90 seconds with 8x throughput). Has also integrated ML model serving into production APIs (churn prediction) using Celery/Redis batching and AWS Lambda/S3, and designed secure multi-tenant architectures with PostgreSQL schema isolation and row-level security.”
Junior AI/ML Software Engineer specializing in LLMs and data-intensive systems
“AI/backend engineer who has owned production applied-ML systems end to end, including a Jitsi meeting intelligence platform with custom RoBERTa boundary detection, LLM summarization, and automated retraining from user feedback. Also has healthcare AI experience building a diabetes medication titration system with strict validation, drift monitoring, and safety guardrails—showing both product speed and high-stakes engineering rigor.”
Senior Python Developer specializing in AWS backend APIs and enterprise authentication
“Backend/data engineer focused on AWS-based Python services and data pipelines: built a Django/DRF user management/auth platform deployed with serverless AWS (Lambda/API Gateway) and event-driven workflows (Step Functions/EventBridge), with CloudFormation + Jenkins for automated delivery and Secrets Manager/Parameter Store for secure config. Also delivered AWS Glue ETL from S3 to RDS with schema evolution controls and incident-driven improvements, and has demonstrated measurable SQL tuning impact (minutes-to-seconds).”
Mid-level Backend & Full-Stack Engineer specializing in distributed systems
“Built a production internal RAG-based Q&A assistant at Huawei for ~4,000 engineers over a 12M-document Elasticsearch corpus, replacing link-only search with synthesized answers and achieving 87% user acceptance while keeping hallucinations under 0.4%. Pairs rigorous offline benchmarking (RAGAS, PR-gated F1 improvements) with human A/B testing and OpenTelemetry-based production monitoring, and also has strong Kubernetes/SRE experience orchestrating 50+ gRPC services with major MTTR and pager-fatigue reductions.”
Mid-Level Full-Stack Software Engineer specializing in cloud-native microservices and FinTech
“Backend/DevOps-focused engineer with healthcare and financial systems experience, including an ICU readmission risk platform delivering real-time ML scores via a secure FastAPI service (PyTorch model serving, PostgreSQL, Celery/Redis) deployed on AWS with strong observability. Has hands-on Kubernetes GitOps delivery (Helm, ArgoCD, HPA) and has supported a JPMC on-prem-to-AWS microservices migration using phased validation and blue-green cutovers, plus Kafka/Avro streaming for real-time transaction processing.”
Junior Full-Stack Software Engineer specializing in AI data systems
“Full-stack engineer with strong DevOps/AWS production experience who builds and operates multi-agent AI systems end-to-end (Streamlit/Python through Docker/Kubernetes and ECS/Fargate). Has delivered measurable outcomes: sub-2s latency and ~92% routing accuracy for an AI wellness assistant, shipped an AI-for-BI prototype in under 6 weeks cutting analysis time ~40%, and improved pipeline iteration speed ~35% via modularization and CI/regression checks.”
Junior Software Engineer specializing in distributed systems and full-stack web development
“Software engineer at Cimpress owning end-to-end transactional pages for Pens.com (e-commerce). Built and integrated new discount experiences in a React/TypeScript + Node.js stack, focusing on modular component architecture to reduce tight coupling and avoid breaking existing functionality; prioritizes roadmap work using performance and conversion metrics.”
Mid-Level Backend Software Engineer specializing in payments, fraud systems, and AI agent infrastructure
“Early-career engineer who owned an end-to-end objective assessment/coding contest platform at an edtech startup, using Postgres + S3 and Redis (queues + ZSET) to decouple and scale code submission processing with worker sandboxes. Also implemented idempotency controls and set up monitoring and CI/CD while the rest of the team focused on curriculum.”
Senior Data Engineer specializing in cloud data platforms and regulated analytics
“Data engineer at Capital One building AWS-based real-time and batch pipelines and backend data services for financial/fraud use cases. Has owned end-to-end pipelines processing millions of records/day, implemented dbt/Great Expectations quality gates, and tuned Redshift/Snowflake workloads (cutting query latency ~22–25% and reducing pipeline failures ~30–40%) while supporting 15+ downstream consumers.”
Mid-level Data Engineer specializing in cloud data platforms and big data pipelines
“Healthcare data engineer with hands-on ownership of claims/member data pipelines on a cloud analytics platform, spanning batch and streaming ingestion (Airflow/Kafka/Spark/Databricks) through serving for reporting. Emphasizes reliability and data quality via embedded validation, schema-drift detection, deduplication, and operational monitoring/incident response, plus pragmatic CI/CD and observability setup in early-stage/ambiguous projects.”
Senior .NET Full-Stack Developer specializing in cloud-native enterprise apps
“Full-stack TypeScript engineer who built and operated a production order/inventory platform (React + NestJS/Node + PostgreSQL) with Redis and RabbitMQ for performance and background workflows. Emphasizes correctness in production via idempotency, retries/backoff, DLQs, and observability, and has also delivered external-facing REST APIs (Swagger, versioning, JWT/RBAC) plus resilient checkout browser automations using Playwright/Puppeteer.”
Mid-level Software Engineer specializing in backend systems, cloud-native apps, and AI platforms
“Backend/full-stack engineer who has owned production systems end-to-end, including a Dockerized Node.js/TypeScript probabilistic fault-tree analysis service for nuclear safety research deployed on AWS. Also built and operated a FastAPI-based RAG pipeline over 200+ PDFs using FAISS, focusing on low-latency, idempotent workflows and strong observability; experienced with API design and Playwright E2E automation across React/Angular projects.”
Mid-level Software Engineer specializing in backend microservices and real-time streaming
“Built and owned an end-to-end LLM-powered enterprise retrieval pipeline at ServiceNow, spanning ingestion of structured/semi-structured sources through vector retrieval and real-time API serving. Focused heavily on reliability and quality (multi-stage validation, monitoring, evaluation pipelines) while also driving performance improvements (~35% faster responses) via caching, async processing, and SQL/query optimization.”
Mid-level Business Analyst specializing in healthcare and retail analytics
“Analytics professional with experience across retail and healthcare, including Kroger and CVS Health. They have built SQL and Python workflows to clean and operationalize messy data, and have owned patient adherence/retention analytics projects that informed targeted interventions and improved refill and retention reporting.”
Senior AI/ML Engineer specializing in predictive analytics and NLP
“ML/AI engineer with hands-on experience building production healthcare AI systems across predictive modeling and GenAI. They built an end-to-end patient risk prediction platform and a RAG-based clinical summarization feature, combining strong NLP/LLM skills with AWS deployment, monitoring, drift detection, and reusable Python service design to deliver measurable clinical and operational impact.”
Entry-level Software Developer specializing in full-stack and AI systems
“Currently at Berryble AI, this candidate is building an LLM-based real-time interview analysis engine using FastAPI, WebSockets, fine-tuned models, and GCP/Cloud Run. They stand out for using AI and agent workflows pragmatically to accelerate development while keeping human ownership over architecture, security, reliability, and maintainability, and they are also pursuing a master's in applied machine learning.”
Mid-level Software Developer specializing in FinTech and cloud-native microservices
“Full Stack Engineer in fintech (JPMorgan) who owns products end-to-end across React UIs and Spring Boot/Kafka backends, with a strong track record of shipping quickly while maintaining reliability via testing, monitoring, and feature flags. Has hands-on experience scaling microservices for high-volume transactions and debugging production latency using ELK/CloudWatch, plus built an internal Python/Flask automation tool adopted by backend engineers to speed API validation and debugging.”
Mid-level SRE/DevOps Engineer specializing in cloud infrastructure and Kubernetes
“Full-stack engineer who has owned an AI-powered HTTP monitoring dashboard end to end, from Node.js/MongoDB backend and dashboard UI through deployment and reliability controls. Particularly strong in turning raw technical signals into usable AI-assisted product experiences, with concrete impact including ~60% faster anomaly detection and meaningful AI cost optimization.”
Mid Software Engineer specializing in cloud-native healthcare and security systems
“Frontend engineer with Oracle Cerner experience building healthcare operations UIs where accuracy, compliance, and workflow efficiency matter. They’ve owned a sophisticated React-based patient record validation and merge interface and also show solid performance instincts through render optimization, state management, and TypeScript-based API modeling.”
Senior Full-Stack .NET Developer specializing in FinTech and Healthcare
“Backend-focused engineer with strong .NET/Angular experience building enterprise financial and healthcare systems, including microservice APIs deployed with Docker/Kubernetes and AWS ECS. Demonstrates production reliability skills across secrets management (Secrets Manager/IAM), incident response (CloudWatch + Kafka failover), and data engineering patterns from SSIS ETL (data quality, incremental recovery), plus proven SQL tuning with a 10-minute report reduced to under 30 seconds.”
Mid-level Full-Stack Java Developer specializing in APIs and cloud microservices
“AI/LLM engineer who has shipped a production document-intelligence agent that automated internal support workflows using RAG, tool calling, and robust fallback controls. Stands out for combining hands-on architecture with measurable business impact: 85% faster query resolution, 35% lower LLM cost, 40% fewer LLM calls, and enough automation to avoid adding 2-3 support engineers.”
Mid-level Backend/Platform Engineer specializing in data pipelines, reliability, and AI-assisted ingestion
“Backend engineer who built and scaled a blockchain-based e-voting platform at early-stage startup Elemential Labs, balancing decentralization with real-world operability by centralizing control-plane components while keeping the ledger immutable. Has hands-on experience migrating high-throughput ingestion from Kafka to AWS Kinesis with parallel cutover, strengthening data integrity and read-after-write consistency (Elasticsearch), and hardening pipelines against silent data-quality failures via anomaly detection and self-healing automation.”