Pre-screened and vetted.
Mid-level Software Engineer specializing in machine learning and full-stack AI systems
“Built production-grade Python systems in a medical/imaging context, including an image feature extraction and survival prediction microservice with strong testing, validation, and observability practices. Also developed a Playwright-based autonomous job application agent that handled dynamic UIs and anti-bot challenges with stealth tooling, proxies, and human-in-the-loop escalation.”
Mid-level Software Engineer specializing in AI-powered full-stack systems
“Backend-focused engineer with experience at AWS building a global alarm processing platform (Python, Lambda/SQS/DynamoDB) handling traffic spikes and reliability issues; resolved duplicate alerts and latency under load by fixing hot partitions and enforcing idempotency. Previously at Cognizant, built Java/PostgreSQL backend workflows for healthcare dashboards using pre-aggregated summary tables, strong SQL optimization, and state-driven job orchestration with ELK-based observability and production guardrails.”
Mid-level Full-Stack Engineer specializing in AI and FinTech platforms
“Full-stack engineer building real-time internal banking operations dashboards (Java/Spring Boot microservices + React/TypeScript) with Kafka-based streaming and post-launch performance optimizations. Also shipped a production internal AI support assistant using RAG (Confluence/PDF/support docs ingestion, embeddings + vector DB retrieval) with guardrails, evaluation loops, and observability to reduce hallucinations and prevent regressions.”
Mid-Level Software Development Engineer specializing in distributed microservices on AWS
“LLM/agent engineer who has shipped multiple autonomous, multi-step agents to production (document-to-SOP conversion, test generation, code generation) using a custom Python DAG orchestrator with persistent state, tool-calling permissions, and structured outputs (Pydantic/JSON Schema). Demonstrates strong production hardening practices—semantic contracts, golden-dataset prompt regression tests, circuit breakers, and multi-level monitoring—and delivered large productivity wins (34 hours of manual writing reduced to ~20 minutes review; ~15–20 engineering hours/week saved).”
Mid-Level Software Engineer specializing in AI/ML and Cloud-Native Microservices
“Research assistant at Syracuse University who owned a Python/FastAPI analytics backend for user-uploaded large datasets, using S3 streaming uploads and background workers for heavy processing. Has hands-on experience deploying Dockerized Python/Java microservices to AWS EKS with Jenkins-based CI/CD, plus Kafka-based event-driven pipelines and practical migration patterns (dependency mapping, dual-write, reconciliation) to minimize downtime.”
Senior Software Engineer specializing in backend infrastructure, cloud automation, and reliability
“End-to-end deployment owner for Oracle document delivery/print services in a hospital-like production environment, focused on reliability/performance at scale (thousands of systems). Also describes implementing event-driven RAG/agentic LLM workflows with attention to embeddings/index consistency, latency, and measurable improvements in response relevance and operational efficiency.”
Senior Software Engineer specializing in full-stack SaaS and Healthcare IT
“Product-minded software engineer from Gusto who has shipped internal AI agent capabilities and end-to-end user-facing features across frontend and backend. Stands out for making pragmatic architecture tradeoffs—moving from OpenAI prototypes to deterministic systems when accuracy, latency, and uptime mattered—while still improving LLM workflow quality and reducing tool-calling errors by 15%.”
Senior Distributed Systems Architect specializing in backend platforms and FinTech
“Full-stack engineer who built an AI-powered visual product discovery feature end to end across web, mobile, backend, and ML integration. Particularly strong in TypeScript-first monorepo architecture, serverless AWS microservices, and productionizing computer vision/LLM pipelines with monitoring, prompt refinement, and human-in-the-loop quality controls.”
Mid-level Software Engineer specializing in cloud, backend, and healthcare systems
“Full-stack engineer with hands-on ownership of a customer-facing advanced performance metrics experience in the Amazon S3 console, spanning React UI, Python/Node services, Redshift/RDS data access, and AWS IaC/CI-CD with CloudWatch/Route53 operational readiness. Demonstrates strong production instincts around resilience (partial failures, multi-region inconsistencies), progressive rollouts/feature flags, and reliable ETL/integration patterns (idempotency, backfills, reconciliation).”
Mid-level Full-Stack Software Engineer specializing in FinTech microservices
“Robotics software engineer who has built end-to-end pipelines spanning backend/data processing through model interfaces and hardware integration. Has hands-on ROS2 experience building Python nodes and debugging real-time behavior via profiling, publish-rate tuning, and latency fixes, plus experience standardizing multi-robot communication with QoS adjustments. Uses Gazebo simulation and Docker/CI/CD to catch integration issues early and speed iteration.”
Mid-level Backend Software Engineer specializing in search infrastructure and AWS microservices
“Search/backend engineer with hands-on experience improving Apache Solr-based search systems end-to-end (indexing strategy changes, ETL updates, and Java/Spring Boot Search API work). Demonstrated production rigor with QA partnership, A/B testing, and rollback-safe kill switches, plus measurable product impact (e.g., +1.5% add-to-cart) and operational troubleshooting including traffic/security mitigation.”
Mid-Level Full-Stack Software Engineer specializing in AWS cloud and microservices
“Backend/LLM engineer who built a production-critical Amazon Bedrock + RAG correction and compliance layer for employee communications, integrating tightly with existing Spring Boot/AWS microservices to reduce manual review while keeping outputs explainable and auditable. Also designed an event-driven system processing 10M+ events/day (SQS/Lambda/DynamoDB/Elasticsearch) and handled on-call incidents with strong observability and reliability patterns (idempotency, retries, hotspot mitigation).”
Mid-level Backend Software Engineer specializing in Python APIs and payment systems
“Backend/ML systems engineer with Stripe payments experience who built an asynchronous processing upgrade handling millions of API requests, cutting peak latency ~20–25% while preserving strict financial consistency via idempotency-safe retries and robust validation/fallbacks. Also built scalable ETL pipelines for messy CSV/Excel/API data with strong observability (structured logging/monitoring) and reliability mechanisms.”
Principal Software Architect specializing in cloud platforms, data engineering, and enterprise security
“Engineering leader with experience defining solutions from business requirements through detailed specifications and implementation, emphasizing cost-aware technology selection. Has led architectural changes including adding IBM Cloud alongside AWS for budget reasons and integrating caching/messaging to improve availability and performance, and describes scaling distributed teams via experienced DevOps/QA hires and structured evaluation.”
Mid-level Software Engineer specializing in cloud-native systems and Android development
“Application-focused software engineer with experience at Amazon and Motorola shipping production systems ranging from developer monitoring/on-call tooling (Alcazar, ~40% MTTR improvement) to consumer AI features used by 100K+ users. Currently building an AI/ML-driven platform with a Python/FastAPI backend on AWS (ECS/RDS/S3) and has handled real production latency/scaling incidents end-to-end.”
Senior Data Engineer specializing in cloud data platforms and real-time pipelines
“Data engineer focused on reliability and observability, building end-to-end pipelines processing millions of records/day from sources like S3 and Kafka. Has hands-on experience with Airflow-based data quality automation, PySpark/Databricks transformations, and shipping versioned Python REST APIs deployed via Docker/Kubernetes with CI/CD (Jenkins) and monitoring (CloudWatch/Azure Logs).”
Mid Backend Software Engineer specializing in cloud-native microservices
“Product-minded software engineer with experience shipping AI-powered financial insights (spend forecasting, cashflow, credit optimization) and building real-time analytics systems using React/TypeScript and FastAPI. Has designed microservices with RabbitMQ/gRPC and strong observability (Prometheus/Grafana/OpenTelemetry), and also built an internal Figma plugin adopted by designers that reduced export time by 50%.”
Junior Full-Stack Engineer specializing in cloud, AI, and distributed systems
“Full-stack engineer from early-stage startups who has owned AI products end to end, from B2B document intelligence platforms on AWS to an HVAC voice assistant and a GCP-based RAG research system. Stands out for combining hands-on backend/infra depth with team leadership in lean environments, and for shipping scalable AI systems that contributed to roughly 1 million yuan in sponsorship.”
Mid-Level Full-Stack Software Engineer specializing in Java/Spring, AWS, and Angular
“Amazon engineer who owned customer-facing Alexa features and built automation-heavy delivery practices (API/service-level testing in CI/CD) to ship quickly without sacrificing stability. Also built an internal self-service feature management/beta access platform (Angular + Spring Boot + event publishing) that replaced a multi-team ticket workflow with instant, auditable operations, and has deep microservices/Kafka experience with strong observability and reliability patterns.”
Senior Full-Stack Developer specializing in cloud-native microservices
“Java full-stack developer who has owned data-intensive, customer-facing and internal web products end-to-end (React/Angular + Spring Boot), including CI/CD and production support. Demonstrates deep microservices experience with RabbitMQ/event-driven architecture, idempotency, DLQs, and compensating logic to maintain reliability and data consistency at scale, plus a track record of replacing spreadsheet-based ops reporting with an adopted real-time internal tool.”
Senior Software Engineer specializing in data infrastructure and reporting platforms
“Backend/data platform engineer who owned a production merchant-activity aggregation and event publishing system processing ~500k merchants daily. Built a Snowflake-based daily KPI summarization pipeline orchestrated via AWS Glue/SQS and an ECS Spring Boot publisher that encrypts and publishes events to Kafka, with strong operational monitoring and reconciliation. Drove major scalability wins (10x throughput) via caching around encryption/key-management and designed selective reprocessing to handle late-arriving data cost-effectively.”
Mid-Level Backend Software Engineer specializing in FinTech and scalable APIs
“Backend/microservices engineer with fintech loan-lifecycle experience operating low-latency (sub-250ms) services in production using Kafka, idempotent transaction design, and Datadog observability. Also built an end-to-end LLM chatbot (React + Flask) with a decoupled model integration layer (FLAN-T5 via Hugging Face) and has experience designing partner-facing REST APIs with OAuth2/JWT and Swagger documentation.”
Mid-level Software Engineer specializing in Java/Spring Boot, Kafka, and AWS
“Software engineer who owned an end-to-end self-service reporting workflow (secure APIs, async/batched processing, and React UI), improving report generation performance by ~30–40% and reducing manual support effort. Also built a RAG/embeddings prototype over internal docs and service logs with grounding-focused guardrails, and has a strong reliability/observability mindset (retries, DLQs, CI/CD validation, dashboards/tracing) for distributed workflows.”
Junior Software Engineer specializing in backend systems, AI, and search
“Built a complex graph-based search engine to find connections between people and has hands-on experience designing multi-agent coding pipelines that move features through implementation, test generation, testing, and sanity checks. Stands out for treating AI agents like an engineering team, with shared-memory coordination, queue signaling, and completeness-focused guardrails to improve reliability and reduce ambiguity.”