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Vetted CUDA Professionals
Pre-screened and vetted.
Rushir Bhavsar
Intern AI/ML Engineer specializing in LLMs, MLOps, and distributed training
“Founding AI engineer (June 2024) at Talon Labs who built and productionized an LLM-powered chatbot for interacting with proprietary supply-chain documents, deployed at large scale (25–100,000 users). Experienced with RAG/LLM orchestration (LangChain, LlamaIndex, Groq AI) and production ops tooling (Kubernetes, Docker, Kubeflow, Airflow), with a metrics-driven approach to evaluation, observability, and stakeholder alignment.”
Jianbo Wu
Mid-level AI/ML Engineer specializing in LLM reasoning and inference optimization
“Former Field Application Engineer at TestTorrent supporting proprietary hardware deployments. Built and integrated customer-requested benchmarking (including MLPerf-style work) by adapting GPU benchmark implementations and validating performance against NVIDIA baselines, plus delivered a backend fix that enabled customer performance testing and helped close a deal. Also has experience running models on AWS/GCP with multi-instance cost optimization techniques like memory offloading.”
Mukundan Sridharan
Executive Technology Leader (CTO) specializing in IoT sensing, AI/ML, and RF/embedded systems
“Currently a startup CTO who thrives on building new technology stacks and rapidly turning technical ideas into products. Interested in partnering with a CEO/business team to commercialize embedded/edge concepts such as multi-sensor drone localization (video/audio/RF with SDR), low-cost solar+battery power nodes networked via LoRa, and an Amazon Sidewalk/LoRa connectivity device with cloud management.”
Soham Patil
Junior Cloud & AI/ML Engineer specializing in AWS GovCloud and MLOps
“Robotics software engineer with hands-on ROS 2 autonomy experience on an obstacle-avoiding quadrotor (ROS 2 + Gazebo + PX4 + Nav2/SLAM), including custom work to extend Nav2 into a 3D aerial domain and output PX4 trajectory setpoints. Also built cost-saving ML infrastructure (PostgreSQL + AWS data-cleaning pipeline) and improved object detection accuracy by 40% using CUDA/PyTorch, with strong containerization and CI/CD practices (Docker + Kubernetes, aggressive version pinning) to prevent environment drift.”
“Built and deployed a production RAG-based internal knowledge assistant that let analysts query company documents in natural language, using LangChain/LangGraph with Pinecone and a FastAPI service for integration. Emphasizes reliability in production through hallucination mitigation (retrieval tuning + prompt guardrails) and measurable evaluation/monitoring (accuracy, latency, task completion, hallucination rate), iterating based on user feedback.”
Nikitha Ramesh
Junior Product Manager and AI/ML engineer specializing in enterprise SaaS and cloud AI
“Growth-focused B2B SaaS operator with hands-on experience improving enterprise adoption for a cloud governance and FinOps platform. They combine customer discovery, ROI-driven messaging, automation, and funnel instrumentation to improve conversion and handoffs, citing an 18% lift in enterprise adoption and roughly $200K-$3M in influenced pipeline.”
Hamidreza Lotfalizadeh
Mid-level AI/ML Engineer specializing in LLM agents, RAG, and ML systems
“At Inertia Systems, built a production LLM-powered ingestion pipeline that converts heterogeneous sources (PDF/JSON/IFC/SQL and financial tables) into standardized text and uses GraphRAG to construct a knowledge graph with verified dependency relationships. Also has hands-on HPC orchestration experience with SLURM, including creating a custom wrapper process manager to improve resource utilization under restrictive scheduling policies.”
Vadivelan Murugesan
Junior Robotics Engineer specializing in ROS 2, perception, and motion planning
“Robotics software engineer/researcher (master’s work) who built a human-aware motion planning stack for a UR16/UR16e arm: RGB-D 3D skeleton perception in ROS2, deep-learning-based human motion prediction, and MoveIt2-integrated real-time planning with a Gazebo digital twin. Demonstrated strong real-time optimization (profiling + GPU offload with CuPy/TensorRT) and practical systems skills spanning safety validation, visualization, and low-level comms (CAN/SocketCAN) on embedded deployments (Jetson, Docker, Autoware/Ouster).”
Kunal Kulkarni
Intern AI/ML Researcher specializing in computer vision and data engineering
“Built a production-oriented multimodal RAG "Fix Assistant" with FastAPI, Tavily search, BM25 + cross-encoder reranking, and a local Phi-3.5 model, emphasizing strict grounding and fallback/verification modes to prevent hallucinations. Also has hands-on federated learning experience using STADLE to orchestrate edge-node training and aggregation for EV telemetry data, plus experience communicating AI results to non-technical stakeholders (traffic RL/congestion outcomes).”
Wilson Harron
Director-level AI/ML & Computer Vision Engineer specializing in robotics and multimodal AI
“Candidate is not currently pursuing entrepreneurship (no business plan and no capital raised) and is not familiar with the VC/accelerator landscape. They show pragmatic, problem-first thinking about evaluating startup ideas—prioritizing real customer pain points and the quality of the founding team—and are open to working for others rather than founding "at all costs."”
Harsha vardhan reddy Yerranagu
Junior Machine Learning & Edge AI Engineer specializing in IoT and robotics
“Robotics/ROS2-focused early-career engineer who built a stereo visual-odometry SLAM system for autonomous navigation and optimized it to run reliably in real time on Raspberry Pi. Strong in sensor fusion (camera+IMU), ROS2 debugging/profiling, and distributed robotics/IoT pipelines (ROS2 + MQTT + cloud), with added experience extracting WiFi CSI for sensing/localization and shipping via Docker + GitHub Actions CI/CD.”
Jahnavi Lasyapriya Vavilala
Junior Machine Learning Engineer specializing in LLMs, NLP, and computer vision
“Built a production, agentic multi-agent pharmaceutical intelligence system for US oncology (breast cancer) conference/news intelligence, automating MSL-style information gathering and summarization for pharma and healthcare stakeholders. Uses CrewAI + LangChain orchestration, custom scraping across ~15 pharma newsrooms, and a grounding-score evaluation approach (sentence transformers/cosine similarity) to mitigate hallucinations.”
Saniya Shinde
Mid-level Data Scientist specializing in NLP, LLMs, and RAG systems
“Built and deployed a production-style vision-language pipeline that generates structured medical reports from chest X-rays using BioViLT embeddings, an image-text alignment module, and BiGPT fine-tuned with LoRA, delivered via Streamlit and hosted on AWS EC2. Also collaborating experience presenting EDA findings, feature importance, and model performance to Ford managers while working with vehicle parts data at Bimcon.”
Sharan Raj Sivakumar
Senior Software Developer specializing in AI/ML automation and cloud-native systems
“ML/MLOps practitioner who built production systems for telecom network analytics, including an automated labeling + multi-label Random Forest solution that cut labeling effort by 90% and sped up RCA. Led an Ericsson auto-deployment platform using Airflow, Azure IoT Hub, Docker, and Celery to orchestrate 120+ containerized ML/rule-based deployments, saving ~80 hours of setup per deployment.”
Connor White
Junior Machine Learning Engineer specializing in GPU-accelerated computer vision
“Robotics software lead from Texas A&M Aggie Robotics who built WoopLib, a SLAM-based vision/navigation library using PID pure pursuit. Has hands-on ROS/ROS2 and Jetson Nano experience integrating Intel RealSense (T265/D435i) with wheel odometry for accurate state estimation, including compiling deprecated sensor support from source and optimizing by moving to Python with C++ bindings and serial streaming to a microcontroller.”
Paavani Karuturi
Junior Backend and ML Engineer specializing in distributed systems and LLM infrastructure
“Backend engineer with strong ownership across authentication, API infrastructure, and AI-powered document workflows. They built and operated a production auth microservice supporting 10,000+ users with measurable latency and security improvements, and also shipped hackathon and applied-AI systems including legal document and medical document retrieval/Q&A products.”
Mid-level Robotics Engineer specializing in teleoperation and autonomous manipulation
Intern Research Engineer specializing in robotics, 3D perception, and reinforcement learning
Senior Robotics Software Engineer specializing in perception, motion planning, and real-time systems
Mid-Level Software Engineer specializing in microservices, cloud platforms, and observability
Intern Software Engineer specializing in autonomous driving middleware and real-time robotics systems