Vetted Unit Testing Professionals

“Walmart engineer who built and shipped a production LLM+RAG system to automate triage and analysis of computer support chats/tickets, producing grounded, schema-constrained JSON outputs for summaries, urgency, and routing recommendations. Emphasizes reliability (hallucination control, confidence thresholds, human-in-the-loop) and runs end-to-end pipelines with Airflow and AWS-native orchestration, plus rigorous evaluation and monitoring tied to business KPIs.”

“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.”

“Built a production agentic AI system to automate data science work using a layered architecture (executive-summary handling, tool-based execution, and on-the-fly code generation). Demonstrates strong end-to-end agent development practices including RAG with vector databases, prompt engineering, and multi-method evaluation (LLM-as-judge/human/code-based), plus Airflow-based orchestration for ML data pipelines and close collaboration with business end users.”

“Backend/platform engineer building HIPAA-compliant, real-time healthcare systems: owned a Python/Flask API layer for an AI-enabled patient engagement and risk scoring service, implemented PHI-safe logging and cross-service auditability, and delivered Kubernetes microservices via ArgoCD GitOps. Also has experience with Kafka streaming pipelines and hybrid cloud-to-on-prem migrations in regulated healthcare/fintech environments.”

“Built and shipped a production LLM-powered RAG assistant for enterprise internal document search (PDFs, knowledge bases, structured data), addressing real-world issues like noisy documents, hallucinations, and latency with grounded prompting, retrieval-confidence fallbacks, and performance optimizations. Also partnered with compliance and business teams at JPMc to deliver a solution aligned with regulatory constraints, supported by monitoring, feedback loops, and systematic evaluation.”

“Robotics software engineer/researcher at Stanford PDML Lab building VisualFT, a ROS2-based visual-tactile sensing system for compliant force-control guidance in acupressure/ultrasound-style manipulation. Also interned at Neocis (dental robotics) improving safety-critical collision detection using Bullet Physics with automated validation and CI (Jenkins/CDash).”

“Sole creator of a full-stack C++ 3D drone simulator/visualizer, including a self-contained flight stack (GNC, sensor simulation, flight manager) plus OpenGL-based graphics/UI. Built SITL-ready interfaces via MAVLink over UDP and ROS 2 Humble, and has embedded Linux CI/CD experience using CMake and Jenkins with automated unit/integration testing.”

“Full-stack developer who built a learning management web app end-to-end using React, Spring Boot, and MySQL, integrating APIs via Axios and validating/testing with Postman. Has experience handling data-heavy workloads (courses, quiz results) and improving performance with pagination, and is comfortable designing microservice-style endpoints with CI/CD considerations.”

“AI/ML engineer (Cognizant) who built a production, real-time credit card fraud detection platform combining deep-learning anomaly detection with an LLM-based explanation layer. Strong focus on regulated deployment: addressed class imbalance and feature drift, and added guardrails (SHAP/structured inputs, fine-tuning on analyst reports, rule-based validation) to keep explanations accurate and compliant. Orchestrated the full pipeline with Airflow + Databricks/Spark and used MLflow/Prometheus plus A/B and shadow deployments for measurable reliability.”

“ML/LLM engineer who has shipped a production transformer-based document understanding system on AWS, owning the full pipeline from domain fine-tuning to Dockerized CI/CD deployment. Demonstrates strong production rigor—latency optimization (distillation/quantization, async batching, autoscaling), orchestration with Airflow/Step Functions/Azure Data Factory, and monitoring/drift detection—plus experience translating ops stakeholder needs into adopted AI automation via dashboards.”

“Data/ML engineer with experience across pharma (Dr. Reddy Laboratories) and financial services (Cincinnati Financial, Capital One), building production NLP and entity-resolution systems that connect messy unstructured text with enterprise SQL data. Delivered semantic search with BERT + vector DB and domain fine-tuning (reported ~35% relevance lift), and builds robust pipelines using Airflow/dbt/Spark with strong validation, monitoring, and stakeholder-aligned rollout practices.”

“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.”

“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.”

“Built and shipped a production LLM-powered multi-agent system that autonomously generates and publishes YouTube videos end-to-end (trend discovery, script writing, image/caption generation, timestamped video assembly). Emphasizes production readiness with extensive automated testing, Redis/Postgres/TimescaleDB state orchestration, and Prometheus/Grafana monitoring, reporting ~100x faster content production and improved engagement/viewership.”

Chris Ferrario

Screened

Principal Gameplay/AI Programmer specializing in game AI and gameplay systems

London/Zurich18y exp

“Gameplay/AI engineer with end-to-end ownership of racing-sim AI (fair physics parity, competitive overtaking) and strong iteration tooling (live tuning + config). Has shipped networked VR multiplayer work at Ready At Dawn, including a consensus-style goal validation approach to reconcile non-deterministic replication/smoothing discrepancies, plus experience with Havok/Bullet, spline math (Catmull-Rom), and complex creature/boss animation behaviors.”

Vivek Reddy

Screened

Mid-level Data Scientist/Data Engineer specializing in ML pipelines, insurance and healthcare analytics

Los Angeles, CA7y exp

“Built a production assistive-vision iPhone app to help visually impaired users find grocery items, training a custom YOLO detector on 2,000+ self-collected/annotated images and deploying via CoreML with a cloud multimodal LLM for navigation instructions. Brings hands-on AWS serverless + ECS container deployment (CDK/GitHub Actions) and a disciplined approach to AI workflow reliability (state-machine design, offline evals, stress tests, logging/metrics), plus experience communicating model insights to non-technical stakeholders (MOTER Technologies).”

Gagan Reddy Konani

Screened

Mid-level Machine Learning Engineer specializing in LLMs and RAG for healthcare

Remote, USA2y exp

“AI Engineer (Medtronic) who deployed a production RAG-based clinical assistant grounded in curated biomedical literature (no patient-identifiable data). Deep hands-on experience orchestrating and hardening LLM workflows with LangChain/LangGraph, including stateful agentic flows, rigorous testing, and evaluation; reports a 72% accuracy improvement through retrieval enhancements (query rewriting, multi-query expansion, MMR reranking).”

Sakshi Dinesh Deore

Screened

Mid-level Software Engineer specializing in AWS, DevOps automation, and data platforms

Bellevue, USA3y exp

“Engineer with Securonix experience deploying and operating production microservices and real-time data-processing systems at high throughput. Led AWS infrastructure, CI/CD, monitoring, and customer-driven customization for a threat-report classification solution, including rule adjustments and model retraining based on live client feedback.”

Geetha Sri Kasu

Screened

Mid-level Software Engineer specializing in Java/Spring Boot, Kafka, and AWS

5y exp

“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.”

“Platform/infrastructure-focused engineer with hands-on experience automating AWS EKS upgrades in Python, building observability around logs/metrics/SLOs, and migrating Terraform delivery from CodePipeline to Atlantis. They describe shipping automation that handled 140 resources across 50+ Terraform files ahead of schedule and debugging a Karpenter/CoreDNS regression in Kubernetes by tracing it from regional traffic spikes to a missing local DNS cache configuration.”

Mustafa Ali

Screened

Mid-level Software Engineer specializing in backend systems and AI

Boston, MA4y exp

“Founding/early backend engineer at ATICA Global who built the company's core hotel property management platform from an empty repo into a production system used by 500+ hotel clients. Combines strong backend architecture with pragmatic product execution, including a dynamic pricing engine that increased partner revenue 17% YoY and an AI-powered sentiment pipeline processing 200K+ reviews/month.”

“Autonomous driving engineer from iFLYTEK who shipped 5+ middleware modules for vehicles across three models, with deep experience in reliability, IPC performance, and real-world system hardening. Stands out for translating flaky production behavior into measurable signals—resolving 30+ faults, cutting backlog 39%, improving latency 20%, and supporting 500+ hours of road testing with 99%+ reliability.”

“ML/AI engineer with strong end-to-end production ownership across predictive ML and Generative AI use cases. They built a churn prediction platform that cut churn 12% and preserved about $1.2M in annual revenue, and also shipped a RAG-based support assistant that reduced ticket resolution time 30% while improving agent satisfaction and onboarding speed.”

Yash Patel

Screened

Mid AI/ML Engineer specializing in MLOps and cloud platforms

Texas, USA3y exp

“ML/AI engineer with strong end-to-end production ownership across classical ML and GenAI systems. Built and deployed predictive analytics and RAG-based internal tools on AWS/Kubernetes with measurable impact on accuracy, latency, deployment speed, safety, and user productivity.”