Vetted Unit Testing Professionals

“Robotics software engineer who built a lightweight, ROS-free distributed control and telemetry stack for a Caltrans long-range culvert inspection robot. Strong in integrating heterogeneous hardware (UART motor controllers, Ethernet sensors, MJPEG cameras) and delivering real-time operator data via FastAPI/WebSockets, including reverse-engineering undocumented protocols and debugging network-induced latency with control-loop redesign.”

“ML/LLM engineer at Charles Schwab who built a production loan-advisor chatbot integrated with internal knowledge and loan-calculator APIs, adding strict numeric validation to prevent rate hallucinations and optimizing context to control costs. Also runs ~40 Airflow DAGs orchestrating retraining/ETL/drift monitoring with an automated Snowflake→SageMaker→auto-deploy pipeline, and uses rigorous testing plus canary rollouts tied to business metrics and compliance constraints.”

“ML practitioner with hands-on experience in healthcare time-series modeling (CGM-based blood glucose prediction) including a novel ICA-based blind source separation approach and robust data-cleaning for noisy, missing sensor data. Also built an embeddings + LLM-powered podcast recommendation workflow using YouTube transcript scraping and Vellum AI document indexing, with a strong emphasis on production-grade engineering practices (TDD, monitoring) and realistic rolling validation for forecasting.”

“ML/NLP practitioner who built customer-feedback topic modeling (NMF + TF-IDF) to diagnose chatbot-to-agent handovers and drove product/ops changes that reduced operational costs by 20%. Also developed LSTM-based intent recognition using Word2Vec/GloVe embeddings for semantic linking, and deployed an LSTM autoencoder for fraud anomaly detection that cut false positives by 25% while capturing 15% more fraud in A/B testing.”

“ML/NLP practitioner focused on insurance/claims analytics for a large financial firm, working with millions of fragmented structured and unstructured records. Built production-grade pipelines for entity extraction, entity resolution, and semantic search using Sentence-BERT + vector DB, including fine-tuning with contrastive learning (reported ~15% recall lift) and scalable ETL/containerized deployment on Kubernetes.”

“Full-stack engineer with strong production ownership who built a "Problem Workspace" coding feature using Next.js App Router + TypeScript, combining Server Components for fast initial render with WebSocket-driven real-time execution updates. Demonstrates deep reliability and data-consistency expertise (idempotency keys, Postgres constraints/indexing, EXPLAIN ANALYZE) and has implemented durable async orchestration (Temporal-style workflows) to reduce failures and timeouts under load.”

“Built an AI-driven insurance policy summarization platform at Marsh, taking it end-to-end from messy PDF ingestion/OCR and custom extraction through LLM fine-tuning and AWS SageMaker deployment. Delivered measurable impact (25% reduction in manual review time, 99% uptime) and demonstrated strong production MLOps/LLMOps practices with Airflow/Step Functions orchestration, rigorous evaluation (ROUGE + human review), and continuous monitoring for drift, latency, and hallucinations.”

“Backend engineer with experience at HSBC and Machinations who has delivered major production performance wins (cutting large trade-file upload times from ~13–15s to ~2s) using chunked parallel processing with strong reliability controls. Also built and shipped an applied AI RAG workflow using Langflow + Cohere embeddings + FAISS with hosted/local LLM fallbacks (Hugging Face, Ollama) and production-grade guardrails, observability, and evaluation.”

“AI engineer who shipped a production chat assistant for a storage company by building the underlying RAG-style knowledge base (document ingestion, chunking/embeddings, FAISS vector store) and an admin update interface to keep content current. Also has full-stack delivery experience (Python REST APIs + React/TypeScript UI) and AWS operations using Terraform/Jenkins, including handling a real production performance incident by optimizing DB queries and adding auto-scaling.”

“Full-stack engineer who has shipped and owned production analytics dashboards using Next.js App Router + TypeScript, combining server components for data-heavy pages with client components for interactive charts/filters. Also built a Temporal-orchestrated payment reconciliation workflow with versioning, idempotency, and exponential-backoff retries, and has hands-on Postgres query/index optimization using EXPLAIN ANALYZE.”

“Software engineer with experience at Goldman Sachs and Arizona State University’s Learning Engineering Institute, shipping production backend systems including a vendor equities invoice-generation service designed for extensibility across multiple vendors. Built Django REST + PostgreSQL backends with JWT auth and Pytest coverage, and delivered data-heavy, responsive Angular dashboards; also has exposure to AWS EC2 deployments and GitLab CI/CD automation.”

“Backend/API engineer with ~4 years experience building production services in .NET Core/PostgreSQL/Redis/Docker and optimizing real-world latency issues (claims ~60% response-time improvement). Also built and owned an end-to-end RAG-based AI assistant using Python/FastAPI, OpenAI APIs, and Pinecone, plus agentic workflows with reliability guardrails (retries, confidence thresholds, monitoring). Currently pursuing a master’s degree and targeting a $150k base salary.”

“Data engineer focused on building reliable, production-grade pipelines and external data collection systems on AWS (S3/Lambda/SQS/Glue/EMR) using PySpark/SQL, serving curated datasets to Snowflake/Redshift for finance and fraud teams. Has operated a large-scale crawler ingesting millions of records/day with anti-bot tactics, schema versioning/quarantine, and CloudWatch/Datadog monitoring, and also shipped a versioned REST API with caching and query optimization.”

“Built and deployed an LLM-powered customer support assistant (“Notable Assistant”) focused on automating common post-customer queries while maintaining multi-turn context and meeting scalability/latency needs. Experienced with production orchestration and operations using Kubernetes and Apache Airflow (DAG-based ETL, scheduling, monitoring/alerts), and has partnered closely with customer service stakeholders to align chatbot behavior with brand voice through iterative testing.”

“Backend/data engineer with hands-on production experience building reliable Python FastAPI services on Kubernetes and delivering AWS EKS + Terraform CI/CD with strong secrets isolation and rollback practices. Also built AWS Glue ETL pipelines into S3/Redshift with schema-evolution handling and data-quality controls, modernized legacy analytics into modular Python services with parallel-run parity validation, and has demonstrated SQL tuning impact (minutes to seconds) plus ownership of batch pipeline incidents end-to-end.”

“Backend/data engineer with hands-on production experience building FastAPI APIs secured with JWT and delivering AWS-based data processing solutions using Lambda and ECS Fargate. Has worked with Snowflake/third-party API sources and targets including DynamoDB, S3, RDS, Redshift, and Glue, and uses CloudWatch/X-Ray for monitoring and troubleshooting. Seeking ~$65/hr and is open to onsite work in Bethesda, MD.”

“Engineering leader focused on developer platforms and open-source frameworks/SDKs, with strong community and release-engineering chops. Drove major reliability and DX improvements (30–50% faster release cycles; 2–3x repeat contributors; ~50% faster onboarding) and led an incremental Python monolith to TypeScript event-driven migration using Protobuf contracts, feature flags, and a plugin architecture to preserve backward compatibility.”