Vetted Latency Optimization Professionals

“Built and owned an end-to-end AV operations automation and dashboarding platform for USC event operations, used daily to coordinate hundreds of live events. Delivered a React/TypeScript full-stack system integrating Smartsheet APIs with strong reliability practices (typed contracts, validation/fallbacks, safe rollouts) and experience with queue-based microservice patterns (idempotency, retries, DLQs, monitoring).”

“Built and deployed a production Retrieval-Augmented Generation (RAG) platform in a healthcare setting to automate clinical documentation review and summarization, targeting near-real-time, explainable outputs. Emphasizes grounded generation to reduce hallucinations, latency optimizations (chunking/embedding reuse), and PHI-safe workflows with access controls, plus strong orchestration experience using Apache Airflow.”

“Worked on Sophia the humanoid robot, building production animation pipelines and enhancing human-robot interaction via perception and behavior orchestration. Experienced in stabilizing noisy perception-driven state transitions and designing smooth, user-centered behavioral flows, collaborating closely with artists, animators, and experience designers to translate creative intent into measurable system behavior.”

“AI/ML practitioner with Northern Trust experience who has shipped production LLM systems (internal support assistant) using RAG, vector databases, orchestration (LangChain/custom pipelines), and rigorous monitoring/feedback loops. Also built AI-driven fraud detection/risk monitoring solutions in a regulated financial environment, emphasizing explainability (SHAP), audit readiness, and stakeholder trust through dashboards and clear communication.”

“At JP Morgan Chase, built and deployed a production LLM-powered RAG knowledge assistant to help fraud investigators and risk analysts quickly navigate regulatory updates and internal policies, reducing investigation delays and compliance risk. Strong focus on secure retrieval (RBAC filtering), reliability (layered testing + observability), and production constraints (latency/SLOs), with Airflow-orchestrated, auditable ML pipelines.”

“Built and productionized an NLP-powered customer support assistant at JPMorgan Chase for digital banking, focused on reducing response time for repetitive client queries. Strong in real-world AI deployment challenges—sensitive data handling, low-latency FastAPI services, and AWS/Kubernetes operations with CI/CD—plus a metrics- and guardrails-driven approach to reliable AI workflows.”

“Backend/platform engineer with payments domain experience, having owned core services for MasterCard’s global card tokenization and settlement platform. Built Django/Celery microservices plus Kafka/Redis real-time fraud streaming, delivering 27% latency improvement, sub-100ms fraud checks, and 18% fewer false positives. Strong DevOps/IaC background across Kubernetes, AWS ECS, Terraform, GitHub Actions, and GitOps practices for high-scale transaction systems (including UPI at PhonePe).”

“Robotics/ADAS engineer who built an assistive feeding robot with reliable 3D mouth tracking (RealSense + MediaPipe) and ROS 2 integration to a WidowX250s arm, solving depth-noise, timing, and workspace/singularity issues for stable low-latency behavior. Also optimized a real-time lane-keeping controller at Hyundai using signal logging/replay, filtering (LPF/Kalman), and feedforward+PI tuning, with experience across SIL/HIL and CAN-based ECU integration.”

“Engineering manager with recent hands-on technical leadership in vendor-based geo augmentation, including making a key pivot from a broken vendor SDK to an internal data ingestion approach. Previously shipped impactful Python microservice refactors that reduced unnecessary data processing/storage and improved runtime payload efficiency, and has owned on-call incidents through mitigation (scaling pods) and prevention process changes.”

“Built and shipped an end-to-end podcast recommendation system exposed via a Flask API and React UI, explicitly balancing relevance, diversity (MMR), and safety constraints while meeting ~200ms latency targets. Also implemented a production-style RAG/information-extraction pipeline using web retrieval, spaCy NER, and fine-tuned SpanBERT with guardrails and evaluation loops (precision/recall/F1) to tune confidence thresholds and improve reliability.”

“Robotics software engineer with autonomous driving lab experience at UCSD, building and optimizing ROS2 perception and control pipelines (camera-based real-time object detection) with a strong focus on low-latency performance and robust message interfaces. Also brings production deployment experience from Hewlett Packard Enterprise, using Docker and Kubernetes for containerized environments and deployment pipelines.”

“Robotics engineer/team lead who built an autonomous lunar rover for the NASA Lunabotics challenge, owning electrical systems, comms (UART/WiFi), and autonomy using RealSense + custom YOLO on a Jetson Orin Nano. Also has extensive ROS 2 experience, including creating a ROS2+Gazebo environment with ros2_control custom controllers for a large-scale additive manufacturing printer and research/thesis work in multi-robot coordination and robustness.”

“Systems/robotics-oriented software engineer focused on real-time orchestration and reliability: built a central control layer coordinating multiple concurrent agents with safe state machines, failure isolation, and recovery. Has hands-on ROS/ROS 2 integration experience in simulation (DDS/QoS, lifecycle, nodes in Python/C++) and emphasizes observability (structured JSON logs, correlation IDs) and low-latency control-loop performance under load.”

“Computer science engineer and master’s graduate who independently built a mechatronics-heavy capstone prototype: a smartphone concept for deafblind users using micro-actuator arrays for braille reading. Also has platform engineering experience at Quantiphi, deploying webhooks to Kubernetes and implementing GitOps-based CI/CD using AWS CodeCommit/CodeBuild and ECR.”

“AI/ML engineer with production experience building an LLM-powered resume-to-job matching and feedback product using RAG, with a strong focus on latency, hallucination control, and scalable deployment. Experienced orchestrating ML inference and backend services on Kubernetes and applying rigorous evaluation/guardrail practices; also partnered with business/product stakeholders at Walmart to improve an NLP-based supplier support system.”

“Built and deployed a production LLM/RAG knowledge assistant integrating internal docs, wikis, and ticket histories to reduce tribal-knowledge dependency and repetitive questions. Emphasizes reliability via grounding + a validation layer, and achieved major latency gains (>50%) through vector index optimization, caching, quantization, and selective re-validation. Comfortable orchestrating end-to-end LLM/data workflows with Airflow, Prefect, and Dagster, including monitoring and alerting.”

“Built and shipped a production LLM-powered recruiting workflow that ranks resumes against job descriptions, generates evidence-based justifications, and finds "hidden fit" candidates using embeddings + RAG. Demonstrates strong production engineering around hallucination control, latency, and predictable LLM cost management (budget checks, top-K pruning, tenant caps), plus orchestration experience with Airflow/Prefect/Kubernetes and a structured evaluation/monitoring methodology for AI agents.”

“Built and deployed an AWS-based LLM/RAG ticket triage and knowledge retrieval system (Pinecone/FAISS + Step Functions + MLflow) that cut support resolution time by 20%. Demonstrates strong production focus on hallucination reduction, PII security, and low-latency orchestration, with measurable evaluation improvements (e.g., ~25% grounding accuracy gain via re-ranking) and proven collaboration with support operations stakeholders.”

“AI engineer with robotics perception experience at Forterra, building and deploying moving-object/obstacle detection models into real-time robot pipelines. Addressed training crashes/latency via sub-batch training and optimizer tuning, and improved debugging using ROS/ROS2 tooling with 3D voxel visualization and color-coded validation.”

“UPenn robotics researcher who architected a real-time autonomous driving decision-making engine, integrating LSTM trajectory prediction with MPC in CARLA and adding conformal prediction to deliver 95% statistical safety guarantees under strict latency constraints. Also built and debugged an autonomous quadrotor stack with ESKF-based 6-DoF tracking and optimized A*/Dijkstra planning to eliminate latency-induced instability, with experience bridging heterogeneous simulation/control systems.”

“UC Berkeley CS–trained hands-on engineering leader with executive experience spanning fundraising and board/customer communication. Led architecture and roadmap for AI-driven fintech platforms (including portfolio data, market signals, document processing, and Bitcoin trading), scaling global orgs (~100 people) and driving modular API-based designs that improved reliability, onboarding speed, and customer retention.”

“Built an LLM multi-agent “ingredient safety” analyzer for cosmetics that cuts consumer research time from ~20+ minutes to minutes, using LangGraph orchestration, hybrid retrieval (Qdrant + Tavily), and safety-focused critic validation (false rejections reduced ~30%→~8%). Also has research-internship experience building computer-vision pipelines to classify emerald color/clarity by translating gem-expert heuristics into quantitative model features.”

“Applied ML to physical EV fleet systems at ST Labs, building a real-time CNN-LSTM fault prediction pipeline from streaming vehicle telemetry and addressing live data alignment issues via resampling/interpolation and buffered inference. Also developed a V2G/G2V energy transfer algorithm to automate charging/discharging for profit optimization, and made high-impact low-latency pipeline decisions at Astera Holdings using profiling, replay testing, and live A/B validation.”