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Vetted Latency Optimization Professionals
Pre-screened and vetted.
Junior ML Engineer specializing in search, retrieval, and recommendation systems
Harshit Vashisth
Junior AI Engineer specializing in LLMs, RAG systems, and MLOps
“Robotics software engineer who built an end-to-end system ("justmatrix"), focusing on multi-agent orchestration and a multi-RAG retrieval backend/API. Has hands-on ROS experience, including a custom node for reliable high-frequency sensor data routing, plus deployment automation using Docker, Kubernetes, and CI/CD.”
Aliman Orucov
Junior Unity Game Developer specializing in gameplay systems, NPC AI, and XR interaction
“Unity/C# developer with experience shipping to Meta Quest and building small Unity test games (e.g., Catch The Fruits and a Finger Game) to validate VR glove interactions. Leans toward structured production workflows and uses Unity documentation plus AI tools to resolve scripting/engine issues.”
Priyanka Palnati
Entry-Level Computer Science Graduate specializing in ML, data analytics, and cybersecurity
“Built a Smart Resume Screening tool with a React frontend and a Python backend, owning most backend architecture and delivery. Implemented FastAPI endpoints for file upload and NLP/ML inference, created the end-to-end resume classification pipeline, logged predictions to a database for accuracy tracking, and deployed a Dockerized service optimized for low-latency, concurrent processing.”
Mid-level AI/ML Engineer specializing in LLMs and RAG systems
Diego Hernandez
Entry-level Mechatronics Engineer specializing in robotics, automation, and ROS 2
“Robotics software/embedded engineer who helped build an end-to-end perception sensor platform in a two-person team, owning PCB design, ROS architecture (sensor/processing/diagnostic nodes), and documentation. Experienced integrating heterogeneous sensors over CAN with Arduino and optimizing real-time performance bottlenecks (camera and high-frequency streams) using compression, grayscale pipelines, and reduced inference frequency; also containerized the system with Docker.”
Mid-level AI Software Engineer specializing in LLM agents and RAG systems
“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”
Akshay Amudhan
Entry Machine Learning Engineer specializing in quantitative finance and DeFi
“Built and deployed a production RAG chatbot using a vector database + LangChain-orchestrated pipeline, focusing on grounded, context-aware responses. Demonstrates practical trade-off thinking (retrieval quality vs latency/cost), hallucination control, and iterative improvement through logging, manual review, and stakeholder feedback loops.”
Joe Francia
“Gameplay engineer with Unity/C# experience across AR and multiplayer, including building a custom dynamic navigation system for an AR "floating island" game (with profiling-driven optimization from A* to Markov-chain movement). Also designed an LLM-assisted conversation system for Sims-like gameplay that constrained choices for reliability and low latency, and shipped Photon Quantum multiplayer features with practical desync/prediction fixes.”