7 Tensorrt Jobs

We are seeking a highly skilled Generative AI Consulting Director to join our dynamic team, where they will lead our consulting team, manage the delivery of consulting services, guide clients through the implementation of our Gen AI platform, and ensure the successful adoption of the platform across industries. Key Responsibilities: Lead or mentor a global team of AI consultants, solution architects, and professional services teams. Develop and execute the strategy for consulting and professional services for the Gen AI platform. Manage the end-to-end Implementation our platform in client environments, ensuring high quality implementations, on time delivery, and alignment with customer expectations. Work closely with clients to understand their business challenges and design tailored solutions using the Gen AI platform. Lead the development of solution architectures, ensuring that proposed solutions are scalable, innovative, and aligned with the client's objectives. Collaborate with product development, GTM, and engineering teams to ensure successful implementation and integrations. Provide feedback to product teams based on client needs and market trends to continuously improve the platforms offerings. Drive client success by ensuring that Gen AI platform implementation deliver measurable value and return on investment (ROI). Work closely with clients to define successful metrics, track project outcomes, and guide the optimization of AI models and systems post-implementation. Manage P&L for the Consulting and Professional Services division, ensuring profitability through effective project management, cost control, and client retention. Develop and implement strategies to drive revenue growth within the professional services arm. Ethical and Responsible AI: Adhere to ethical AI practices, such as fairness, transparency, and accountability. Address biases and potential risks associated with AI systems to ensure responsible deployment and usage. Research and Innovation: Stay updated with the latest advancements in AI technologies, frameworks, and algorithms. Conduct research and experimentation to explore innovative approaches and techniques that can enhance AI capabilities. Mandatory Qualifications/Skills: A bachelors or masters degree, or equivalent, in computer science, Artificial Intelligence, or a related field. 15+ years of experience in consulting or professional services, with at least 5 years in a leadership role overseeing a team of AI consultants or solution architects Extensive experience in delivering Generative AI solutions and familiarity with AI platforms, including knowledge of NLP, deep learning, and reinforcement learning. Experience with large language models (LLMs) and prompt engineering. Solid understanding of various fine-tuning techniques like full fine tuning, PEFT techniques like LoRA, QLoRA and the strategy to adopt for various use cases Proficiency in languages such as Python, Scala, or Java In depth knowledge of both relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., Vector databases, MongoDB, Cassandra etc). Expertise in Gen AI/AI libraries / frameworks, including but not limited to LangChain, LangGraph, LangSmith, TensorFlow, PyTorch, scikit and Keras Proven understanding of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and experience deploying AI models on these platforms. Proven experience in managing client relationships and understanding their business needs to deliver successful AI solutions. Strong understanding of AI systems architecture and the ability to design and implement complex AI solutions for clients across various industries. Experience with project management methodologies, and a proven ability to manage large, complex projects to successful completion. Excellent leadership, mentoring, and team building skills, with a track record of developing high performing teams. Strong business acumen, with the ability to balance technical expertise with client centric decision making. Outstanding communication and presentation skills, capable of engaging with senior executives and non-technical stakeholders. Strong problem solving and analytical skills, with the ability to think creatively and provide innovative solutions Preferred Skills: Knowledge of NVIDIA CUDA, cuDNN, TensorRT, and experience with NVIDIA GPU hardware and the software stack. Familiarity with High Performance Computing (HPC) and their integration of AI workloads. Familiarity with Big Data platforms and technologies, such as Hadoop or Apache Spark and their integration with AI solutions.

Job Overview We are looking for a Senior Computer Vision Machine Learning Engineer to lead the development of real-time CV/ML systems, with an emphasis on deploying models on edge platforms like the NVIDIA IGX Orin. The ideal candidate will have experience in designing robust vision pipelines, training and optimizing deep learning models, and working closely with hardware platforms for deployment. Responsibilities Lead the design, development, and deployment of end-to-end computer vision and deep learning models Optimize and deploy CV/ML pipelines on edge platforms, particularly NVIDIA IGX (Orin preferred) Work with cross-functional teams to integrate models into real-time applications (e.g., robotics, safety systems, industrial inspection) Develop and maintain datasets, perform data augmentation, and ensure quality training inputs Leverage NVIDIA SDKs (e.g., DeepStream, TensorRT, TAO Toolkit, CUDA) for performance and acceleration Collaborate with hardware engineers to fine-tune models for power, latency, and throughput constraints Stay up to date with the latest research and techniques in computer vision, edge AI, and embedded ML Requirements Bachelors or Masters degree in Computer Science, Electrical Engineering, or related field 5+ years of experience in Computer Vision and Machine Learning (deep learning emphasis) Proficiency in Python, C++, TensorFlow, PyTorch Strong understanding of model optimization techniques for edge deployment Hands-on experience with NVIDIA platforms IGX, Jetson, or Xavier (IGX Orin highly preferred) Experience with NVIDIA SDKs (e.g., DeepStream, TensorRT, CUDA, TAO Toolkit) Solid knowledge of vision tasks: object detection, tracking, classification, segmentation Familiarity with containerization (Docker), CI/CD pipelines, and version control (Git) Preferred Qualifications Experience in industrial AI, medical imaging, or robotics Exposure to RTOS, safety-critical systems, or IEC 61508/ISO 26262 environments Familiarity with ONNX, OpenCV, ROS, or GStreamer What We Offer Opportunity to work on cutting-edge AI/edge technology with real-world impact Collaborative and fast-paced engineering culture Flexible working hours and remote work options Competitive salary and benefits package Location-Remote,Delhi NCR,Bangalore,Chennai,Pune,Kolkata,Ahmedabad,Mumbai,Hyderabad

About The Role Were building an agentic AI platform that turns one line of text and a video feed into end-to-end, real-time computer-vision solutionsthink semantic video search, object / action recognition, and task-oriented visual agents deployable with a single click As a Gen AI ML Engineer, youll architect the core vision & multimodal-reasoning stack and pave the road from prototype to production. Roles And Responsibilities Semantic video search Ship a pipeline that allows users to type show every forklift near aisle 5 in the last 30 minutes and get keyed-off clips in Wire embeddings to a hybrid FAISS/HNSW index; surface results through a simple REST & React playground. Create agentic pipelines Chain vision language models and zero/few-shot vision models with LLM planners (Gemini, GPT-4o, AutoGen, etc.) so a single prompt becomes a multi-step perception workflow. Profile and accelerate inference (TensorRT, ONNX, quantization, batching) to meet latency / throughput targets on GPU and CPU fleets. Rapid prototyping loops Run weekly paper-to-prototype spikes: reproduce a fresh arXiv idea, benchmark, and decide go/no-go in Hand successful python scripts & checkpoints to MLOps for productionizationno plumbing marathons. Data & Evaluation Spin up scalable pipelines for video ingestion, labeling (active learning, weak supervision), experiment tracking, and continuous evaluation. Collaborate & Lead Partner with product and ML Ops engineers; set research direction, mentor future hires, and establish best practices. Must-have Skill Set 13 years deep-learning research experience (internships & grad work count). Fluency in Python + PyTorch; comfortable hacking large vision/LLM repos. Proof you ship ideasfirst-author paper, OSS repo, Kaggle medal, or faithful reproduction of a cutting-edge model. Hands-on with LLM prompting/fine-tuning and at least one agent framework. Able to turn fuzzy product asks into measurable experiments and explain results clearly. Bonus Cred Large-scale video retrieval or temporal grounding experience. Prior work building agentic-AI pipelines that combine perception models with LLM reasoning. Open-source contributions to GenAI/vision libs (OpenCLIP, Vid2Seq, ViperGPT, etc.). What can you expect? Ability to shape the future of manufacturing by leveraging best-in-class AI and software; we are a unique organization with niche skill set that you would also develop while working with us World class work culture, coaching and development Mentoring from highly experienced leadership from world class companies (refer to Ripik.AI website for details) International exposure Work Location NOIDA (Work from Office)

Senior Software Technical Director / Software Technical Director Bangalore Founded in 2023,by Industry veterans HQ in California,US We are revolutionizing sustainable AI compute through intuitive software with composable silicon We are looking for a Software Technical Director with a strong technical foundation in systems software, Linux platforms, or machine learning compiler stacks to lead and grow a high-impact engineering team in Bangalore. You will be responsible for shaping the architecture, contributing to codebases, and managing execution across projects that sit at the intersection of systems programming, AI runtimes, and performance-critical software. Key Responsibilities: Technical Leadership: Lead the design and development of Linux platform software, firmware, or ML compilers and runtimes. Drive architecture decisions across compiler, runtime, or low-level platform components. Write production-grade C++ code and perform detailed code reviews. Guide performance analysis and debugging across the full stackfrom firmware and drivers to user-level runtime libraries. Collaborate with architects, silicon teams, and ML researchers to build future-proof software stacks. Team & Project Management: Mentor and coach junior and senior engineers to grow technical depth and autonomy. Own end-to-end project planning, execution, and delivery, ensuring high-quality output across sprints/releases. Facilitate strong cross-functional communication with hardware, product, and other software teams globally. Recruit and grow a top-tier engineering team in Bangalore, contributing to the hiring strategy and team culture. Required Qualifications: Bachelors or Master’s degree in Computer Science, Electrical Engineering, or related field. 18+ years of experience in systems software development with significant time spent in C++, including architectural and hands-on roles. Proven experience in either: Linux kernel, bootloaders, firmware, or low-level platform software, or Machine Learning compilers (e.g., MLIR, TVM, Glow) or runtimes (e.g., ONNX Runtime, TensorRT, vLLM). Excellent communication skills—written and verbal. Prior experience in project leadership or engineering management with direct reports. Highly Desirable: Understanding of AI/ML compute workloads, particularly Large Language Models (LLMs). Familiarity with performance profiling, bottleneck analysis, and compiler-level optimizations. Exposure to AI accelerators, systolic arrays, or vector SIMD programming. Why Join Us? Work at the forefront of AI systems software, shaping the future of ML compilers and runtimes. Collaborate with globally distributed teams in a fast-paced, innovation-driven environment. Build and lead a technically elite team from the ground up in a growth-stage organization. Contact: Uday Mulya Technologies muday_bhaskar@yahoo.com "Mining The Knowledge Community"

URGENT Freelance Trainer Opportunity: NVIDIA-Certified Trainer for Advanced Programming Course (1 Week) We are seeking NVIDIA-certified trainer to deliver a week-long, in-depth classroom class room training session on advanced programming for NVIDIA DGX systems. The training will focus on the following key areas: Training Topics: Docker Containers : Introduction to the concept and practical use for model deployment and environment management. Model Training : Deep dive into model training techniques and workflows using NVIDIA DGX systems. GPU Memory Distribution & ONNX : Understanding GPU memory management and how ONNX facilitates cross-platform model deployment. TensorRT & Triton : Concepts behind TensorRT for model inference optimization, and using Triton for model serving and deployment. Hands-on Lab : Practical exercises on implementing TensorRT and Triton in real-world scenarios. Advanced Optimization Techniques : In-depth strategies for optimizing deep learning models on GPUs for performance improvements. Future Trends in GPU Computing : Exploration of the latest advancements and future directions in GPU-based computing, AI, and machine learning. Requirements: Must be an NVIDIA-certified trainer with proven expertise in advanced GPU programming. Availability to conduct a full one-week training course (5 days). Strong hands-on experience with NVIDIA DGX , Docker , TensorRT , Triton , and ONNX . Ability to deliver both conceptual learning and practical lab sessions effectively. If you have the required expertise and are available for this one-week on-site session engagement, please reach out with your credentials and relevant experience. Mail ID : Sapna.raturi@softelnetworks.com

Role Summary: As the Edge AI Lead , you will oversee the design, optimization, and deployment of AI and machine learning models on edge devices, including NVIDIA Jetson, STM microcontrollers, and other embedded platforms. This role requires expertise in real-time operating systems (RTOS), the Robot Operating System (ROS), and hardware accelerators such as NPUs, TPUs, CPUs, and GPUs. You will lead a team to deliver high-performance, low-latency AI solutions and collaborate closely with robotics, embedded, and mechanical teams to create efficient, real-time autonomous systems. Core Responsibilities: 1. Design and Development of Edge AI Models Lead Edge AI Model Development: Drive the design and development of AI models optimized for edge devices and real-time processing. Focus on applications like object detection, SLAM (Simultaneous Localization and Mapping), sensor fusion, and predictive maintenance. Hardware-Aware Model Optimization: Develop models with an understanding of the constraints and capabilities of different hardware accelerators, including NPUs (Neural Processing Units), TPUs (Tensor Processing Units), CPUs, and GPUs. Optimize models through quantization, pruning, and knowledge distillation to fit the processing capabilities of edge devices. Integration with RTOS and ROS: Ensure models are compatible with real-time operating systems (RTOS) for reliable, time-sensitive applications. Leverage ROS for seamless integration with robotic systems, enabling efficient communication between AI models and control systems. 2. Deployment and Integration of Edge AI Models Edge Device Deployment: Lead the deployment of AI models on edge devices, including platforms like NVIDIA Jetson, STM32 microcontrollers, ARM Cortex, and Qualcomm Snapdragon. Ensure seamless integration with RTOS and sensor interfaces for reliable real-time processing. Coordinate with Robotics and Control Teams: Collaborate with robotics and embedded control teams to ensure AI models are effectively integrated within robotic platforms. Address challenges related to sensor placement, communication protocols, and power distribution. Optimize Data Flow with ROS and RTOS: Ensure AI models are well-integrated with ROS for real-time data exchange and with RTOS for deterministic performance. Optimize communication pathways between sensors, processors, and AI models for smooth data flow. 3. Edge AI Pipeline and MLOps Implement MLOps for Edge AI: Develop MLOps pipelines specifically tailored for edge deployment, including CI/CD, automated testing, and performance monitoring. Ensure models can be continuously updated, retrained, and redeployed based on operational data. Model Lifecycle Management: Manage model versioning, deployment, and rollback strategies to ensure reliable, up-to-date models are deployed on edge devices. Develop feedback loops to monitor model performance and enable continuous improvement. Real-Time Monitoring and Troubleshooting: Implement monitoring tools to track metrics such as latency, accuracy, and resource usage in real time. Develop protocols for troubleshooting issues to maintain reliability and performance in production environments. 4. Hardware Acceleration and Performance Optimization Leverage Hardware Accelerators: Use tools like NVIDIA TensorRT, CUDA, OpenVINO, and Qualcomm AI Stack to maximize performance on GPUs, TPUs, NPUs, and other hardware accelerators. Optimize AI inference to take full advantage of the specific architecture of each edge device. Low-Level Programming for Efficiency: Utilize low-level programming languages like C, C++, and assembly to optimize code execution on embedded systems. Implement custom kernels and optimize memory usage to improve performance on constrained hardware. Energy and Power Optimization: Focus on energy-efficient model deployment to maximize battery life and reduce power consumption, particularly for mobile and autonomous robots. Apply power management strategies to balance performance and energy efficiency. 5. Leadership and Team Management Lead and Mentor the Edge AI Engineering Team: Build and guide a team of Edge AI engineers, providing technical direction, mentorship, and hands-on support. Set clear performance expectations, provide regular feedback, and support professional growth. Project and Resource Management: Oversee project timelines, resource allocation, and task prioritization for the Edge AI team. Ensure alignment with broader organizational goals and coordinate with cross-functional teams to achieve project milestones. Foster a Culture of Innovation and Technical Excellence: Promote a culture of continuous learning, experimentation, and technical rigor within the team. Encourage knowledge-sharing and collaboration to solve complex edge AI challenges. 6. Collaboration with Cross-Functional Teams Work Closely with Embedded Systems and Software Teams: Collaborate with embedded systems and software engineers to ensure compatibility of AI models with embedded platforms, RTOS, and other system software. Address integration challenges related to memory constraints and real-time requirements. Coordinate with Robotics, Data, and Mechanical Teams: Work closely with robotics engineers, data scientists, and mechanical engineers to align on data needs, model integration, and design constraints. Ensure that Edge AI solutions support and enhance the capabilities of the robotic systems. Align with Head Robotics and AI Software Leadership: Collaborate with the Head Robotics and AGI/DL Software Head to ensure Edge AI development aligns with broader AI and robotics objectives. Ensure edge solutions support real-time perception, decision-making, and autonomous functionality. 7. Innovation and Research in Edge AI and Embedded Systems Explore Emerging Edge AI Technologies: Stay current with advancements in edge AI hardware, software, and frameworks, including low-power inference engines, neural accelerators, and lightweight edge computing frameworks. Assess and implement new technologies to enhance performance and efficiency. Lead R&D Projects on Edge AI Challenges: Initiate research projects to address unique edge AI challenges such as low-power image recognition, real-time SLAM, and multi-sensor fusion on edge devices. Lead proof-of-concept projects to validate new approaches and methodologies. Contribute to Open-Source and Industry Engagement: Encourage team participation in open-source projects and engagement with the AI/ML community. Promote contributions to Edge AI tools, libraries, and frameworks to stay connected with industry trends and drive community-driven innovation. 8. Compliance, Safety, and Quality Assurance Ensure Safety and Compliance Standards: Work closely with quality and compliance teams to ensure that Edge AI models meet industry safety standards, particularly for autonomous and industrial applications. Quality Control and Documentation: Implement quality control processes for Edge AI models, ensuring reliability, consistency, and long-term performance. Maintain comprehensive documentation on deployment protocols, optimization techniques, and troubleshooting procedures. Develop and Enforce Best Practices: Create and maintain best practices for edge AI development, deployment, and maintenance. Ensure protocols are well-documented, accessible, and regularly updated. Required Qualifications: Education: Bachelor's or Master's degree in Computer Science, Electrical Engineering, Robotics, or a related field. Advanced degrees or certifications in AI, embedded systems, or robotics are preferred. Experience: 8+ years of experience in AI, machine learning, or embedded systems, with at least 3 years in edge computing or embedded AI. Demonstrated experience deploying and optimizing AI/ML models on edge devices, with a focus on low-latency, high-performance environments. Strong experience in leading teams and managing projects involving edge AI, embedded systems, or robotics. Technical Skills: Machine Learning and AI: Proficiency in ML/DL frameworks (e.g., TensorFlow Lite, PyTorch, ONNX) with extensive experience in model optimization for edge devices. Real-Time Systems (RTOS) and ROS: Experience working with real-time operating systems (RTOS) and the Robot Operating System (ROS) to enable reliable real-time processing and seamless communication in robotic systems. Hardware Acceleration and Low-Level Programming: Expertise in using tools like TensorRT, CUDA, OpenVINO, and low-level programming languages (e.g., C, C++) to optimize AI inference on hardware accelerators (NPU, TPU, CPU, GPU). Embedded Systems and Edge Platforms: Deep knowledge of edge computing platforms such as NVIDIA Jetson, STM32 microcontrollers, ARM Cortex, and Qualcomm Snapdragon. Understanding of RTOS, middleware, and embedded programming. Preferred Qualifications: Robotics and Autonomous Systems Knowledge: Understanding of robotics control systems, perception systems, and integration of AI within autonomous systems. Energy-Efficient AI: Experience with optimizing AI models for low-power applications, particularly for mobile and autonomous robotics where power consumption is critical. Project Management and Agile Practices: Familiarity with Agile methodologies and experience in project management to oversee complex, cross-functional projects effectively. Open-Source and Community Engagement: A track record of contributions to open-source AI/ML or embedded systems projects and active participation in the Edge AI community.

Responsibilities Develop and deploy computer vision algorithms and deep learning models for diverse problems. Design and implement computer vision models using state-of-the-art techniques and frameworks. Explore and analyze unstructured data like images through image processing. Analyze, evaluate and optimize existing computer vision systems to improve performance and accuracy. Test and validate computer vision code and models, ensuring robustness and reliability. Research and implement new computer vision technologies to stay at the forefront of the field. Collaborate with cross-functional teams to develop innovative solutions that meet project requirements. Monitor the performance and accuracy of computer vision models, making necessary adjustments and improvements. Maintain and update computer vision systems to ensure their continued functionality and relevance. Provide technical support and guidance to team members and customers using computer vision systems. Requirements 3 - 5 years of experience as a Computer Vision Engineer. Bachelor's degree in Computer Science, or a related field. Proven experience in developing computer vision systems, including hands-on implementation and deployment. Strong knowledge of computer vision algorithms, libraries and tools, such as OpenCV, TensorFlow, PyTorch, Keras, NumPy, scikit-image, PIL, Matplotlib, Seaborn, etc. Familiarity with tools and libraries commonly used in computer vision projects such as CUDA, OpenCL, OpenGL. Expertise in various computer vision projects, including object detection, image classification, text detection & OCR, face detection, generative models, video analytics, object tracking and model compression/optimization. Knowledge of runtime AI frameworks like ONNX, TensorRT, OpenVINO. Experience in cloud platforms (AWS, Azure), Docker, Kubernetes and GitHub. Experience in training models through GPU computing or on the cloud. Familiarity with machine learning and deep learning concepts and frameworks. Excellent problem-solving skills and the ability to think analytically. Good written and verbal communication skills for effectively communicating with the team and ability to present information to varied technical and non-technical audiences. Ability to work independently and in a fast-paced environment and also be able to work in a team when required. Desired Candidate Profile Experience: 3 - 5 years Location: Bangalore/Coimbatore Qualification: Computer Science or a related field Job Type: Full-Time, Permanent Schedule: Day Shift, Monday to Friday Workplace Type: On-site (Work from Office) Notice Period: Immediate