Vetted Unit Testing Professionals

“Backend engineer who shipped a Spring Boot transactions/points feature while using AI selectively for boilerplate and test ideas, keeping core system design decisions in human hands. Stands out for rigorous validation of AI output against database behavior, concurrency, idempotency, and failure scenarios, and has also built a lightweight planner -> code gen -> validator agent-style pipeline with step-level logging and retries.”

Janki Mehta

Screened

Senior Frontend React Developer specializing in scalable web applications

Fremont, CA10y exp

“Frontend-focused product engineer in healthcare who has owned patient/provider scheduling experiences end-to-end, from product requirements and API design through React implementation, deployment, and post-launch measurement. Particularly strong in real-time operational dashboards, performance optimization, and cross-functional work with backend and product teams on complex scheduling workflows.”

“Frontend/product engineer with strong Angular and TypeScript experience who has owned internal products like a configuration-driven reporting platform and bulk-operation workflows end to end. Stands out for combining product ownership, backend/API collaboration, and AI-powered analytics work, including a Gemini-based sentiment dashboard that helps the business mine user feedback for retention and subscription insights.”

“Front-end engineer with hands-on ownership of a React/TypeScript workforce management platform used by site managers and field workers in the energy sector. Stands out for building scalable, reusable UI architecture for complex task/checklist workflows while also improving real-world usability and performance through pagination, bulk actions, and close feedback loops with operational users.”

“Full-stack and AI product engineer with strong AWS/Snowflake experience who built an internal feature flag platform and helped migrate a cybersecurity insights product into a multi-agent AI chat interface. They report production scale of 1M+ embeddings and 50k+ monthly queries, with outcomes including an 80% reduction in analyst work and dashboard generation in 7 minutes; the work was also featured by Claude and AWS.”

“Early-career/new-grad candidate who built TrendScout AI, an evidence-first market intelligence agent that ingests messy news, extracts entities/events, builds a Neo4j knowledge graph, and answers questions via RAG with citations. Achieved ~95% retrieval relevance by combining ChromaDB semantic search with graph-based retrieval and validating outputs through human evaluation and guardrails to prevent hallucinations.”

Mayur Jain

Screened

Mid-level Full-Stack Engineer specializing in AI, blockchain, and cloud systems

Akron, OH5y exp

“Founding engineer who has built core product and backend architecture from scratch at two startups, spanning a full-stack NFT marketplace and an AI voice assistant for sales qualification and scheduling. Brings 5+ years of full-stack experience with strong React/TypeScript and Python backend depth, plus hands-on production experience with LLM workflows, evaluation, and AI-assisted developer tooling.”

“Full-stack product engineer with hands-on ownership from UX/design collaboration through React frontend build, FastAPI/Django services, and AWS deployment. Particularly notable for shipping AI-powered customer support experiences using RAG, LangChain, OpenAI, and Pinecone in a banking context, while also showing pragmatic MVP execution on inventory management workflows.”

“Backend engineer who optimized an AI-driven portfolio analytics/insights platform at Fidelity, addressing latency and traffic growth by moving services toward microservices, improving service communication, and tuning API/DB performance. Experienced scaling Python/FastAPI services with Docker + Kubernetes autoscaling, and strengthening security/privacy for sensitive client portfolio data used in LLM-based reporting.”

“Robotics software engineer focused on multi-robot fleet orchestration in ROS 2, owning the fleet manager and task dispatch layer for pick/drop workflows. Strong in real-world reliability and safety (heartbeats, idempotent tasking, E-stop/localization confidence gates) and in debugging timing/state issues via telemetry alignment and rosbag replay, with experience in simulation, CI/CD, Docker, and Kubernetes-based deployments.”

“LLM/ML engineer who has shipped production systems in legal/financial-risk domains at Wolters Kluwer, including a hybrid OCR+deterministic+LLM extraction pipeline that structured UCC filings at massive scale and drove $6M+ in revenue. Also built LangGraph-based multi-agent “Deep Research” workflows with model routing, tool calls (MCP), persistence, and human-in-the-loop review, and partnered closely with policy writers to deliver LLM summarization that cut writing time by ~60%.”

“Backend/data engineer focused on production-grade Python microservices and AWS platforms, including a hybrid Lambda + ECS Fargate architecture managed with Terraform and CI/CD. Has hands-on reliability experience (JWT/OAuth, timeouts, retries, centralized error classification) and built AWS Glue/PySpark ETL pipelines consolidating PostgreSQL/RDS, MongoDB, and S3 sources into curated partitioned Parquet datasets. Demonstrated measurable SQL tuning impact (8 minutes to 25 seconds) and disciplined legacy-to-modern migrations with parity validation and UAT sign-off.”

“Frontend engineer in an insurance SaaS white-label environment who transformed a fragmented ecosystem of 100+ duplicated React repos into a scalable platform using versioned NPM libraries (CommonWeb/WorkflowWeb). Built a JSON-driven, multi-step quote and payment workflow in React+TypeScript with Redux-Saga for complex async orchestration and reliability (idempotency, retries, takeLatest). Delivered measurable impact: 35% bundle reduction, onboarding cut from weeks to days, and bug fixes propagated across clients in hours.”

“Full-stack engineer with financial services experience (Lincoln Financial) who owned a customer-facing financial portal end-to-end using TypeScript/React and Node/Express. Has hands-on microservices and RabbitMQ event-driven workflows, addressing scale issues like retries/duplicates with idempotency and traceable logging, and built an internal real-time ops/support dashboard to improve monitoring and incident response.”

“Significant contributor/maintainer to an open-source JavaScript event-tracking client SDK, owning API consistency/backward compatibility, high-load batching and retry/backoff improvements, and test/CI + documentation upgrades. Diagnosed production-like issues (missing events under load) via reproduction and logging, then reduced GC pressure and improved predictability with a ring-buffer-based batching redesign while actively triaging issues and reviewing PRs.”

“Backend/DevOps-leaning engineer with trading/financial systems experience (AIG), focused on reliability: built Python automated test suites to ~95% coverage and integrated them into CI/CD. Has hands-on Kubernetes microservices deployment with GitOps (ArgoCD), plus experience supporting cloud-to-on-prem migrations and building real-time streaming pipelines resilient to spikes and data loss.”

“Full-stack engineer with production experience building Java 17 Spring Boot microservices for high-traffic systems at Verizon and on a JPMC payments platform (funds transfer/validation using ISO 20022), plus modern React/TypeScript dashboards for ops and analytics. Demonstrates strong scalability and reliability chops (Kafka event-driven pipelines, Redis caching, clustering, BullMQ background jobs) and has built real-time apps end-to-end with secure JWT refresh-token auth and Socket.io performance tuning.”

“Backend/platform engineer with hands-on ownership of high-stakes data migrations in regulated domains (core banking and insurance), including a Python ETL framework that migrated 100,000+ customer records within a cutover window. Strong DevOps/GitOps background deploying Python and Spring Boot microservices to Kubernetes with Helm and ArgoCD, plus real-time Kafka transaction streaming for fraud/analytics with reliability patterns (DLQs, retries, partition tuning).”

“Built production LLM/RAG platforms for financial services to enable natural-language Q&A over large policy/compliance document sets stored in Snowflake and SharePoint. Strong in MLOps and orchestration (Airflow, ADF, Step Functions, MLflow) and in solving real production issues like stale embeddings and model performance, including an incremental Snowflake Streams sync that cut processing time from hours to minutes.”

“Customer-facing software engineer who builds configurable, extensible solutions under vague requirements (e.g., a bin-packing-style resource allocation feature) and iterates via frequent sandbox deployments and customer testing. Has hands-on experience integrating multiple shipping APIs, navigating outdated/unclear documentation, and resolving production issues quickly through log-driven debugging and safe rollback procedures.”

“ML/NLP practitioner focused on financial-services document intelligence and compliance workflows—built an end-to-end pipeline to classify documents and extract financial entities from loan applications, emails, and statements stored in S3/internal databases. Strong in entity resolution/record linkage and in productionizing pipelines with GitHub Actions CI/CD, testing, data validation, and Docker, plus semantic search using OpenAI embeddings and a vector database.”

“ML/AI engineer with defense and commercial analytics experience: deployed a real-time aerial object detection system at Dynetics (YOLOv5 + TorchServe in Docker on AWS EC2) with drift-triggered retraining and 99.5% uptime, tackling ambiguous targets and weather degradation. Previously at Fractal Analytics, built and explained a churn prediction model for marketing stakeholders using SHAP and delivered it via a Flask API into dashboards, driving a reported 22% attrition reduction.”

“NLP/ML practitioner who built an explainable, clinician-aligned system to detect cognitive decline (Alzheimer’s/stroke-related) from audio responses, achieving 97% accuracy on only a few hundred data points. Also has experience with healthcare claims entity resolution and prototyped a word2vec-based patent search vector database in Elasticsearch, with strong emphasis on testing, interpretability, and scalable Python data workflows.”

“ML/NLP practitioner with banking experience (M&T Bank) who has built a GPT-4 RAG system using LangChain and Pinecone to connect unstructured customer data with internal knowledge bases, improving accuracy and reducing manual lookup time by 50%+. Strong in entity resolution and productionizing scalable Python data workflows, including major performance wins by migrating bottleneck joins from Pandas to Dask.”