TensorRT Experts in Germany
matched in minutes from over 15,000 CVs with the power of AI.Hire experts who optimize NVIDIA TensorRT inference, convert and tune models for GPU deployment, and integrate engines into production services. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used TensorRT
Benjamin Matschke
Last position:
Founder, system architect, and main developer at Institute for Artificial Study (IAS)
- Expert-supervised AI systems for scientific reasoning, model evaluation, and research workflows.
- Built the IAS Problem Solver, an orchestrated system for difficult mathematical reasoning; it achieved 84% in one submitted answer set on the Leipzig mathematics benchmark.
- Built a resumable state-machine pipeline for research-grade mathematics benchmark generation: source selection, LLM-agent-based phenomenon discovery, task synthesis, gold-answer and certificate generation and validation, probing, repair, human feedback, and quality gates, targeting tasks that are difficult, natural, verifiable, and cost-effective.
- Current work extends this into budget-aware AI research workflows for real scientific problems with expert review.
Tech stack: Python, OpenAI/OpenRouter-compatible APIs, embeddings, RAG, SQLite.
Hamza Salaar
Last position:
Research Associate - AI & Autonomous Systems at Hochschule Coburg
- Developed and implemented AI-based perception and multimodal systems for real-world environments
- Built, trained, and evaluated Machine Learning and Deep Learning models using Python, PyTorch, TensorFlow, and OpenCV
- Worked with Vision-Language Models (VLMs), Large Language Models (LLMs), transformer-based architectures, and multimodal AI systems
- Applied LoRA-based fine-tuning techniques and experimented with diffusion models for generative and multimodal AI applications
- Developed multimodal perception pipelines using camera, LiDAR, and sensor data
- Designed end-to-end workflows for data processing, model training, evaluation, benchmarking, and robustness analysis
- Utilized HuggingFace Transformers and modern Deep Learning frameworks for AI experimentation and deployment workflows
- Applied GPU-accelerated computing, CUDA-based processing, ONNX, and TensorRT optimization for efficient inference and large-scale model training
- Collaborated with industry partners including Valeo and REHAU on applied AI and intelligent system projects
- Developed scalable AI architectures and prototype software solutions for automation and perception tasks
Ariel Lev
Last position:
Sr. Principal Engineer at Slalom
- Held direct line management responsibility for a team of 4 Platform Engineers — owning hiring, performance reviews, and career development — while establishing a shared engineering standards framework and coaching culture that accelerated delivery across client engagements.
- Led a team of engineers to architect a cloud-native voice AI system for a major inspection client, enabling 2,500 field inspectors to document work fully hands-free via real-time transcription and AI agents — eliminating manual data entry across 440,000 inspections per month and reducing per-user cost from $9 to $1. Stack: AWS (DynamoDB, S3, Transcribe, CloudFront, API Gateway, Bedrock), ElevenLabs, Claude.
- Led a team of engineers to automate multi-region Kubernetes cluster management for a global SaaS leader, reducing provisioning time from 3 weeks to under a day and eliminating 90% of configuration errors. Stack: EKS, Terragrunt, Python, Bash, ArgoCD.
- Accelerator - Cloud-Agnostic AI Platform: Architected and delivered a cloud-agnostic, Kubernetes-native platform as an accelerator, enabling multi-tenant, enterprise-scale management of self-hosted LLMs with concurrent deployment of multiple base models and dynamic LoRA adapter serving. Designed production infrastructure using open-source tooling (ArgoCD, Karpenter, vLLM, SGLang) with automated model lifecycle management, API security (Keycloak + LiteLLM), and cost-optimized GPU provisioning.
Kartik Trivedi
Last position:
Master Thesis Student at Fraunhofer LBF
- Topic: Object Detection and Semantic Segmentation for (AUV) Systems using Transformer-Based Vision Models and Sensor Fusion.
- Designed and implemented an end-to-end multi-sensor fusion perception pipeline (Camera, LiDAR, IMU) in ROS
- Developed CNN-based Machine Learning model (YOLOv8) and Transformer-based vision models for real-time object detection
- Processed and clustered 3D LiDAR point clouds using DBSCAN, RANSAC, and voxel grid filtering to enable robust object localisation in noisy environments.
- Designed Bayesian Network models (GeNle) for probabilistic reasoning and sensor-level decision fusion under uncertainty.
- Applied Kalman filtering for sensor state estimation, temporal alignment, and smooth object tracking, reducing false positives in safety-critical scenarios.
- Evaluated system performance under realistic driving dynamics, improving tracking stability and overall perception robustness.
- Built deep learning pipelines for training, validation, and performance evaluation of perception models using sensor data.
Amr Amer
Last position:
Machine Learning Engineer at German Research Center for Artificial Intelligence (DFKI)
- Developed end-to-end reproducible ML pipelines (PyTorch) with data versioning (DVC), experiment tracking (MLflow), automated testing (PyTest), and CI/CD across all training workflows.
- Scaled Vision Transformer and CNN training across NVIDIA A100 GPU clusters (CUDA, DDP, SLURM); applied hyperparameter optimization (W&B Sweeps) to reduce training overhead and identify optimal configurations.
- Developed a real-time 3D human motion generation system (ViT, VQ-VAE, SMPL-X/PIXIE) for personality-conditioned avatar synthesis; achieved state-of-the-art FID = 6.15 and P-FID = 10.31 on the UDIVA benchmark.
- Validated model expressiveness through structured user studies, achieving 86% accuracy in distinguishing extroverted vs. introverted avatar behaviors.
- Optimized inference pipelines by deploying PyTorch models via TensorRT and ONNX Runtime into native C++ code; benchmarked performance.
Ghaith Ale
Last position:
Lead Perception Engineer at Driving Examiner AI Platform
- Automated driver assessment by programming temporal rule engines to evaluate lane-change execution safety, head-pose mirror checks, indicator usage cycles, and compliance with traffic lights and road signs
- Synchronized real-time traffic sign recognition and multi-state traffic light classification models with time-series CAN-bus telemetry and HD-map spatial priors to grade traffic rule adherence
- Trained and deployed distinct deep learning models optimized for interior cabin monitoring and exterior surrounding-area perception
- Combined perception outputs with camera intrinsics and horizon stability checks to execute 3D ground-plane object distance estimation assuming flat-ground geometry
- Deployed a split-compute edge network across a 10-vehicle fleet via VPN, implementing a zero-allocation host memory pipeline to eliminate frame accumulation latency (6×21 FPS per vehicle)
Dilip Goswami
Last position:
Freelance Computer Vision Consultant at Spiral Physical Therapy Inc.
- Developing methods for monocular 3D facial reconstruction and personalized geometric modelling from mobile imagery
- Building learning-based approaches for facial shape estimation, video-based facial analysis, and privacy-preserving visual learning
Shiqing Fan
Last position:
Technical Director at EmotionPool GmbH
- Spearheaded the EU market entry strategy for L3/L4 autonomous logistics vehicles, driving the technological localization and deployment of the parent company’s smart robotics portfolio.
- Orchestrated technical alignment between top-tier autonomous driving suppliers across China and Europe, translating complex client requirements into precise engineering specifications compliant with EU standards.
- Cultivated strategic joint R&D initiatives with leading European universities, research institutes, and enterprises, accelerating the transition of cutting-edge robotic concepts into commercial products.
- Directed the end-to-end architecture of intelligent warehousing solutions, guiding cross-functional teams in optimizing hardware integration for autonomous vehicles & robots, and overall system performance.
- Led the R&D of high-fidelity simulation and AI algorithms using NVIDIA Isaac Sim & Lab, establishing robust "Sim-to-Real" pipelines to train and validate dynamic path planning optimization, intelligent obstacle avoidance, and complex navigation stacks prior to physical deployment.
- Maintained hands-on oversight of the core system architecture, focusing on bottom-level performance tuning, AI model inference acceleration with TensorRT/ONNX Runtime, and sensor integration.
Oliver Köhn
Last position:
Consultant for data-driven AI solutions at Oliver Köhn - IT-Freelancer
- AI-powered automation with a focus on efficiency, information processing, and assistant systems
- Automated email classification (OpenAI, FastAPI)
- Contract analysis for LegalTech (Llama 3, LangGraph)
- Internal knowledge search with RAG (VLLM, Hugging Face)
- Anomaly detection on edge devices (LLAVA, TensorRT)
- Agent system for management reports (LangGraph, Zapier)
Surya Alla
Last position:
AI Software Engineer at Fraunhofer FIT
- Developed LLM-based automation utilities including structured reasoning pipelines, LLM-as-a-Judge evaluation tools, and multi-model comparison frameworks.
- Built RAG pipelines for internal research workflows using LangChain, ChromaDB, and FastAPI, enabling semantic retrieval and multi-step reasoning.
- Integrated LLM microservices into existing ML systems using Docker, FastAPI, and GitLab CI/CD with reproducible deployment workflows.
- Designed inference APIs combining vision models and LLM reasoning for multimodal analytics and decision-making.
- Optimized embedding-based retrieval using vector store pruning, improved chunking logic, and dynamic retriever selection.
- Performed prompt engineering and system instruction tuning for consistency, robustness, and reasoning quality.
- Built benchmarking suites to evaluate LLM latency, reasoning quality, retrieval accuracy, and robustness under different prompt templates.
Adithya Balaji
Last position:
Edge AI Software Engineer at Neura Robotics GmbH
- Deployed and optimized Vision-Language-Action (VLA) and diffusion policy models on NVIDIA Jetson Orin and Jetson Thor, meeting real-time inference latency targets for humanoid robot control loops.
- Built TensorRT engine pipelines (PyTorch → ONNX → TensorRT) with INT8/FP8 post-training quantization, calibration dataset design, and quantization-aware validation, reducing inference memory footprint by over 3× on Jetson without accuracy regression.
- Developed custom CUDA C++ plugins and CUDA Graphs for latency-deterministic, real-time policy execution – meeting hard runtime and memory constraints on embedded GPU targets.
- Developed an inference engine for VLA models on top of llama.cpp bringing different VLA policies under single runtime, packaging each as a single self-contained GGUF that needs no Python or PyTorch.
- Profiled and tuned GPU execution using NVIDIA Nsight Systems and Nsight Compute, identifying CUDA kernel bottlenecks, memory bandwidth saturation, and SM occupancy issues across Jetson Orin and Thor compute profiles for cross-layer performance optimization.
Discover over 15,000 top freelancers
Statistics of experts using TensorRT
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
1.7 years
Positions per freelancer
7
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Education
Bachelor's degree or higher
100%
Master's degree or higher
91%
Doctorate
27%
Certifications per freelancer
0
Most common languages
German, English, Arabic
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using TensorRT
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Inference speed
TensorRT is NVIDIA's inference optimizer and runtime for running trained neural networks faster on NVIDIA GPUs. Specialists use it to reduce latency, increase throughput, and prepare models for real-time use in production systems.
Common work
- Convert models from PyTorch, ONNX, or TensorFlow for deployment
- Build TensorRT engines for low-latency inference
- Tune precision, batching, and memory use
- Integrate inference into services, edge apps, and pipelines
Ecosystem fit
TensorRT often sits next to CUDA, cuDNN, ONNX, Triton Inference Server, and DeepStream. Strong specialists understand how these pieces work together, especially when the goal is stable GPU inference across servers or embedded devices.
When to hire
Companies bring in freelance experts when model training is done but production performance is not good enough. That is common in computer vision, speech, recommendation, and other systems that need predictable response times.
What strong experts do
Good TensorRT professionals read model graphs, spot unsupported layers, and choose the right precision and engine settings. They also test correctness after optimization, because speed only matters when the output still matches the model intent.
Germany projects
In Germany, TensorRT work often supports industrial inspection, mobility, robotics, and other GPU-heavy products. Remote collaboration is common, but on-site sessions can help when teams need hands-on work with hardware, deployment targets, or internal model pipelines.
Frequently asked questions
Need clarity? These are the questions we hear most often about TensorRT.
TensorRT is used to make trained models run faster on NVIDIA GPUs. Companies hire specialists for inference tuning, engine building, and deployment work when latency or throughput matters more than training.
Yes. TensorRT is the official NVIDIA name, and many searchers also use NVIDIA TensorRT or the short form TRT. The core goal is the same: optimize inference for supported GPUs.
TensorRT is usually chosen when the main goal is maximum inference performance on NVIDIA hardware. ONNX Runtime is broader across back ends, while CUDA alone gives lower-level control but not the same model-level optimization workflow.
A strong TensorRT specialist usually knows ONNX, PyTorch or TensorFlow export paths, CUDA basics, and GPU memory behavior. Experience with deployment tools such as Triton Inference Server or DeepStream is often valuable too.
TensorRT projects usually need more than basic model knowledge. The right person should have handled conversion issues, precision choices, engine serialization, and post-optimization validation on real workloads.
Yes, most TensorRT work can be done remotely if the team can share models, test data, and target hardware details. On-site work in Germany is mainly useful for hardware access, lab debugging, or close work with internal deployment teams.
A strong TensorRT expert can explain why a model is slow, which layer or precision setting causes the issue, and how to verify the fix. They should also be clear about trade-offs between speed, accuracy, and compatibility.
TensorRT freelancers are often brought in for production inference pipelines, edge deployments, vision systems, and performance rescue work after model training is complete. They are also useful when a team must ship on NVIDIA hardware and cannot afford long tuning cycles.
The average hourly rate of freelancers in Germany who have used TensorRT in their recent projects is 92 €, which corresponds to a daily rate of about 737 € based on an 8-hour working day.
Of the freelancers in Germany who have used TensorRT in their recent projects, 100% hold at least a Bachelor's degree, 91% hold at least a Master's degree, and 27% hold a doctorate.
On average, freelancers in Germany who have used TensorRT in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 1.7 years.
The most common languages among freelancers in Germany who have used TensorRT in their recent projects are German (100%), English (100%), and Arabic (18%).
The most common industries among freelancers in Germany who have used TensorRT in their recent projects are Information Technology (100%), Automotive (45%), and Education (36%).
The most common business areas among freelancers in Germany who have used TensorRT in their recent projects are Information Technology (100%), Product Development (91%), and Research and Development (82%).
Main locations of FRATCH Experts, who have recently used TensorRT
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
