Vetted Object-Oriented Programming (OOP) Professionals

“AI/ML engineer currently at BlackRock who deployed an AI-powered sentiment analysis microservice into a task management platform to prioritize and escalate high-risk/frustrated tickets from free-text comments. Experienced running production microservices on AWS EKS with Docker/Kubernetes/Helm and provisioning infrastructure via Terraform, with strong MLOps rigor (MLflow evaluation pipelines, canary rollouts, and real-time monitoring) and cross-functional collaboration with product/operations.”

“Master’s-level candidate with an academic project portfolio, including ownership of a Python-based video game recommendation system using unsupervised clustering. Has hands-on experience designing the system approach and validating recommendation quality with test cases, plus teaching assistant experience instructing Git/GitHub workflows; limited exposure to Kubernetes, GitOps, and large-scale infrastructure.”

“PhD-trained engineer with ~3.5 years of consulting experience building simulation/ML-driven manufacturing software. Deployed an ML surrogate model as a .NET C# DLL integrated with MES workflows, and resolved a critical pre-production latency issue by redesigning serialization/storage. Also built Python-based integrations across CAD/CAE tools and cloud material databases using an XML data model, with a strong interest in digital twins and real-to-sim/sim-to-real robotics workflows.”

“Cornell master’s student (CS/Stats) focused on research-heavy ML projects: implemented a sparsity-driven RL approach (DAPD + Soft Actor-Critic) that maintained stable learning even with ~95% of weights removed in OpenAI Gym continuous-control tasks. Also worked on diffusion-based computer vision with conditioning and latency-focused U-Net choices, and scaled unsupervised community detection on a 50k-node/800k-edge Reddit graph via BFS subgraph sampling.”

“Software engineer with JPMorgan Chase experience building production real-time dashboards for financial risk metrics. Strong full-stack background (React/TypeScript + Node/Express + PostgreSQL) and production operations on AWS (ECS, CloudWatch) with CI/CD and observability tooling; has optimized ingestion and query performance for millions of trading-log records.”

“Unreal Engine (UE5) gameplay engineer who built large-scale open-world RPG systems end-to-end, including world streaming, AI ecosystems, and networked combat. Strong multiplayer focus: used Unreal Insights/custom profilers to identify replication/physics bottlenecks and delivered >40% frame stability improvement via relevancy checks and proxy-physics strategies, plus deterministic fixed-step simulation with rollback/state correction.”

“Software engineer at Cimpress owning end-to-end transactional pages for Pens.com (e-commerce). Built and integrated new discount experiences in a React/TypeScript + Node.js stack, focusing on modular component architecture to reduce tight coupling and avoid breaking existing functionality; prioritizes roadmap work using performance and conversion metrics.”

“Software engineer whose recent work includes co-designing and building a "Shared Profile" feature for a social event-planning app (Again, Sometime). Previously at Pure Storage, set up Docker-standardized Ubuntu/Python environments to simulate hardware testbeds and support workload/performance regression testing for other engineering teams; no robotics/ROS experience.”

“Built and operated a production TypeScript backend for a stateful conversational quoting chatbot at Tringapps, orchestrating multi-step workflows and session state while integrating with Salesforce and NetSuite. Implemented validation/retry logic, modular architecture, and strong logging/observability to handle real-world edge cases and external API failures.”

“Software engineer with strong end-to-end ownership across frontend, backend, data, and infrastructure, including real-time systems (Kafka/Postgres) and observability (Datadog). Built and productionized an AI-native RAG support assistant (OpenAI embeddings + Pinecone) with prompt/guardrail design, achieving 48% agent adoption and 30% faster responses. Experienced in legacy modernization and reliability work using feature flags, event/transaction replay, and rapid embedded delivery.”

“Revamped a university academic calendar system into a Python-based calendar generation service, turning a weeks-long manual scheduling workflow into software that generates dozens of valid calendar combinations in under a minute. Also contributed to an Amazon search ML classifier by introducing precision/recall evaluation to better surface critical failure modes and improve prediction quality.”