Vetted Unit Testing Professionals

“Engineer with hands-on experience owning customer deployments for ordering and billing systems at Amdocs, including performance tuning, CI/CD improvements, and post-launch stabilization that delivered about 50% faster execution time. Also built and debugged an LLM-powered task prioritization app using Gemini, Streamlit, Python, and MongoDB, with a strong focus on prompt reliability, validation, and handling inconsistent real-world inputs.”

“Engineer with a mature, agent-oriented approach to AI-driven software development, using structured planning, TDD, and verification loops rather than ad hoc prompting. Has hands-on experience acting as a tech lead for multiple AI agents in an LLM intelligent routing project, coordinating implementation, testing, debugging, and edge-case review with strong attention to system tradeoffs.”

“Data engineering/software development candidate who built a stock market pipeline and uses that project to demonstrate strong architectural thinking across Kafka, Spark, and Airflow. They stand out for a pragmatic approach to AI: using tools like Copilot, ChatGPT, LangChain, and AutoGen to accelerate development while maintaining human oversight, testing, and system-level decision making.”

“Candidate has hands-on experience using AI-assisted development in a pragmatic, controlled way, including shipping a more user-friendly student feedback form by redesigning text-heavy inputs into checkboxes and dropdowns. They stand out for disciplined review habits: line-by-line validation of AI-generated code, strong edge-case testing, and thoughtful use of structured prompts and staged workflows instead of over-relying on autonomous agent frameworks.”

Abhinava Sai Tirunagari

Screened

Junior Full-Stack Engineer specializing in AI, healthcare, and FinTech systems

Gainesville, FL2y exp

“Frontend-leaning software engineer who built significant parts of an AI platform at Cognura Health, translating complex document-processing and extraction workflows into usable browser interfaces for business and operations teams. Stands out for combining React/TypeScript UI ownership with backend API collaboration, performance tuning, and thoughtful UX for asynchronous AI workflows.”

Michael Adeniyi

Screened

Mid-level Full-Stack & AI Engineer specializing in LLM-integrated cloud applications

New York, NY3y exp

“Built an AI immigration compliance co-pilot for F1 OPT and STEM OPT students, combining rule-based risk assessment with LLM-powered guidance on a React/TypeScript and AWS serverless stack. Stands out for thoughtful handling of high-risk AI: grounding responses in structured compliance data, adding guardrails, and keeping legal interpretation human-in-the-loop. Also contributed to an education-focused AI product for teachers and helped expand it with quiz generation and document editing features.”

“Software engineer using AI pragmatically to accelerate development while keeping human review central to quality. Has hands-on experience applying AI and lightweight multi-agent workflows in a microservices environment spanning Java Spring Boot APIs, React modules, and Kafka event flows, with strong emphasis on architecture validation and production safeguards.”

Prabhav Karve

Screened

Junior Software Engineer specializing in data engineering and AI applications

Rochester, NY4y exp

“Data engineer/automation builder with experience at Rochester Regional Health and Accenture, focused on replacing fragile manual reporting with production-grade Azure, Python, and Snowflake pipelines. Stands out for combining strong systems thinking, rigorous validation, and practical AI/LLM usage to drive measurable outcomes, including a 34% throughput improvement and support for regulatory reporting that helped avoid €150M in penalties.”

“Intern/full-stack developer who built a sports player statistics dashboard with Next.js, React, Node.js, TypeScript, Zustand, and PostgreSQL. Demonstrated solid ownership across the stack, including API design, state consistency, production debugging, query/index optimization, and phased zero-downtime schema migrations.”

“Front-end engineer focused on sophisticated browser UIs, with strong depth in performance optimization, modular architecture, and workflow-heavy product design. Built a reporting platform from scratch and delivered measurable UX wins, including reducing form latency from 300ms to under 12ms and turning a 2.5-hour image-prep workflow into a 5-minute batch process.”

“LLM/agentic-systems practitioner who has repeatedly taken LLM-driven pricing/decision services from prototype to production using pilots, guardrails, observability, and staged rollouts. Demonstrates strong real-time incident troubleshooting (dependency timeouts, cached fallbacks) and post-incident hardening (isolation/async/alerts), and also supports go-to-market via developer workshops, technical demos, and sales-aligned POCs.”

“JavaScript engineer and open-source contributor focused on runtime performance, reliability, and developer experience—refactored a widely used client-side API/state library to improve concurrent request handling, error consistency, and UI performance while adding tests and documentation. Also owned improvements to a core microservice at Velsa integrating multiple hospital systems, bringing structure to ambiguous priorities and delivering stability and performance gains from design through deployment.”

“Unity game developer who built a context-sensitive movement and camera system for a grid-based dungeon crawler and used DOTween for key gameplay animations. Worked at Chimera Entertainment on Songs of Silence, contributing via bug fixes, working within an existing Photon Fusion protocol, and implementing a UI-heavy in-game lexicon; also leverages AI tools (e.g., ChatGPT) to accelerate editor/tooling and gameplay scripting.”

“Built a production GPT-4/LangChain/Pinecone RAG “AI Copilot” at Northern Trust to automate financial report generation and analyst Q&A over internal structured (SQL warehouse) and unstructured policy data. Focused on real-world production challenges—grounding and latency—achieving major speed gains (seconds to milliseconds) via MiniLM embedding optimization and Redis caching, and implemented rigorous testing/evaluation with MLflow-backed metrics while aligning compliance and finance stakeholders for deployment.”

“ML/LLM engineer with production experience building medical RAG systems to automate chart review, including retrieval + re-ranking and rigorous evaluation. Notably uncovered errors/bias in physician-curated ground truth by tracing answers back to source note chunks and presented evidence to an academic partner, accelerating deployment. Also built a RAG-based FAQ chatbot for a health insurance company and delivered it to non-technical stakeholders via demos.”

“LLM/agent engineer who built a production multi-agent research automation system using LangGraph (planner, retriever with FAISS, supervisor, evaluator) with structured outputs and citation tracking for traceable reports. Emphasizes reliability and operations—LangSmith-based observability, multi-level testing, hallucination mitigation, and latency/cost controls—plus prior experience as a Computer Vision Software Engineer at Deepsight AI Labs working directly with non-technical customers.”

“Customer-facing embedded/IoT engineer with experience taking lab prototypes into production, including an IoT medical facility integration where they re-architected firmware with RTOS scheduling, added health monitoring/fault recovery, and implemented secure OTA updates. Strong at real-time diagnosis using end-to-end observability and at tailoring technical demos for both embedded and cloud developer audiences (MQTT/device shadows), helping drive customer confidence and broader deployments.”

“Graduate Assistant Intern at Friends University who built and deployed a GenAI-driven requirement understanding system that automates extraction and semantic grouping of technical requirements from large unstructured documents. Demonstrates strong LLM engineering rigor (golden datasets, regression testing, post-processing validation) and production-minded delivery using LangChain/LlamaIndex orchestration, FastAPI microservices, Docker, and cloud deployment.”

“Cloud/hybrid infrastructure engineer with McKesson experience migrating tightly coupled healthcare applications to microservices on AWS EKS. Strong in IaC-driven standardization, CI/CD automation, and production observability (CloudWatch/Splunk/Prometheus/tracing), with demonstrated ability to debug complex incidents spanning Kubernetes and cloud networking.”

“Backend/data platform engineer who owned a FastAPI + Kafka microservice in Verizon’s billing pipeline, handling high-volume usage ingestion/validation/enrichment with strong observability and CI/CD on AWS EKS. Demonstrated measurable performance gains (latency down to ~120–150ms; Kafka throughput +30–40%; DB CPU -25%) and led an on-prem ETL-to-AWS migration using Terraform, parallel validation, and phased cutover with zero downtime.”

“Full-stack engineer who built "Workstream AI," an AI-powered engineering visibility product that converts GitHub activity into real-time insights using an event-driven microservices stack (RabbitMQ/Postgres/Express) and GPT-4 with a React frontend. Previously a Founding SWE at a health & wellness startup, building data-driven user management tooling, and also delivered a real-time shuttle tracking/ride request system using Java Spring Boot/Hibernate + React; comfortable owning production deployment details (AWS EC2, DNS, SSL).”

“ML/AI engineer with production experience in high-scale banking fraud detection at Truist, building an end-to-end pipeline (Airflow/AWS Glue/Snowflake, PyTorch/sklearn) with automated retraining and Kubernetes-based deployment; delivered measurable gains (22% fewer false positives, 15% higher recall) and reduced manual ops ~40%. Also partnered with clinicians at Kellton to deploy an LLM system for summarizing/classifying clinical notes, improving review time and decision speed.”

“ML/NLP engineer with hands-on experience building production systems for unstructured insurance claims and customer data linking. Delivered measurable impact at scale (millions of documents), combining transformer-based NLP, vector search (FAISS/Pinecone), and human-in-the-loop validation, and has strong production workflow/observability practices (Airflow, AWS Batch, Grafana/Prometheus).”

“ML/NLP engineer (Beyond Identity) who built production semantic search and entity-resolution systems over internal security documentation, using LDA + BERT embeddings with FAISS/Pinecone to cut search time by 30%. Also scaled a real-time anomaly detection pipeline to millions of events/day with Spark and AWS Lambda, with strong emphasis on measurable validation (Precision@k, MRR, F1, ARI).”