Vetted Unit Testing Professionals

“Backend/data engineer with experience on large-scale consumer platforms (Google and Meta), building high-traffic Python microservices (REST/gRPC) on Kubernetes with strong reliability/observability practices. Delivered AWS container-based deployments with CI/CD and IaC, and built AWS Glue ETL pipelines on S3 with schema evolution and data quality controls; also has demonstrated SQL tuning impact (15% latency reduction) and incident ownership for batch pipelines.”

“Highly experienced robotics software engineer building ROS/ROS2 systems for fleets of autonomous mobile robots (Clearpath Jackal/Husky and custom platforms), spanning localization, navigation, and multi-robot coordination. Has published work at ICRA (including RL-based local planning and heterogeneous robot coordination via a ROS-to-ZMQ bridge) and maintains open-source ROS modules, with strong simulation, debugging, and CI/CD practices.”

“Engineering leader/player-coach who built and validated a preference-based recommendation engine using clustering, including generating test data to evaluate how clusters evolve over time. Has SRE/DevOps experience and has owned production incidents end-to-end (logging-driven RCA and refactoring patterns that failed at large data scale). Emphasizes quality and platform stability via unit, integration, and load testing, and has managed performance via regular 1:1s and PIPs.”

“Backend/ML platform engineer with Google experience leading Python microservices for an AI-driven recommendation/retrieval system, including PyTorch inference and a retrieval-augmented generation workflow. Strong in production Kubernetes + GitOps (ArgoCD), real-time Kafka/Spark pipelines, and phased on-prem/legacy to AWS/GCP cloud migrations with reliability-focused rollout and rollback practices.”

“Meta engineer who owned end-to-end production systems for AI-enabled smart glasses, spanning React/TypeScript apps through Node/Java microservices on AWS EKS with Kafka/Postgres. Built and productionized a real-time RAG pipeline (LangChain + OpenAI + Elasticsearch) with rigorous guardrails (shadow/canary, fallbacks, monitoring), delivering major improvements in latency (~35–40%), error reduction (~30%), and engagement (reported +40% DAU).”

“LLM/agent engineer who built a production code-generation agent at Corvic AI that lets non-technical users query CSV/tabular data in natural language by generating and executing Python. Focused on making LLM systems reliable and scalable via schema-aware validation, sandboxed execution-feedback retries, prompt caching/embeddings, async execution, and high-throughput data processing with Polars; also partnered with Adobe product/marketing to ship brand-aligned AI content generation for email and push notifications.”

“Built and deployed a production LLM conversational AI system at OpenAI supporting chat, summarization, and semantic search at 1M+ requests/day, driving major latency (40%) and accuracy (25%) improvements through Pinecone optimization and tighter RAG with re-ranking. Also has Amazon experience improving recommendation systems by translating ML metrics into business terms to boost CTR and conversions, with strong MLOps/orchestration depth (Airflow, MLflow, SageMaker, Kubeflow).”