Looking for challenging role?If you really want to make a difference - make it with us Can we energize society and fight climate change at the same time? At Siemens Energy, we can. Our technology is key, but our people make the difference. Brilliant minds innovate. They connect, create, and keep us on track towards changing the worlds energy systems. Their spirit fuels our mission. Our culture is defined by caring, agile, respectful, and accountable individuals. We value excellence of any kind. Sounds like you? We are seeking a highly skilled and driven Senior AI Engineer to join our team as a founding member, developing the critical data and AI infrastructure for training foundation models for power grid applications. You will be instrumental in building and optimizing the end-to-end systems, data pipelines, and training processes that will power our AI research. Working closely with research scientists, you will translate cutting-edge research into robust, scalable, and efficient implementations, enabling the rapid development and deployment of transformational AI solutions. This role requires deep hands-on expertise in distributed training, data engineering, MLOps, a proven track record of building scalable AI infrastructure. Your new role- challenging and future- oriented Design, build, and rigorously optimize everything necessary for large-scale training, fine-tuning and/or inference with different model architectures. Includes the complete stack from dataloading to distributed training to inference; to maximize the MFU (Model Flop Utilization) on the compute cluster. Collaborate closely and proactively with research scientists, translating research models and algorithms into high-performance, production-ready code and infrastructure. Ability to implement, integrate & test latest advancements from research publications or open-source code. Relentlessly profile and resolve training performance bottlenecks, optimizing every layer of the training stack from data loading to model inference for speed and efficiency. Contribute to technology evaluations and selection of hardware, software, and cloud services that will define our AI infrastructure platform. Experience with MLOps frameworks (MLFlow, WnB, etc) to implement best practices across the model lifecycle- development, training, validation, and monitoring- ensuring reproducibility, reliability, and continuous improvement. Create thorough documentation for infrastructure, data pipelines, and training procedures, ensuring maintainability and knowledge transfer within the growing AI lab. Stay at the forefront of advancements in large-scale training strategies and data engineering and proactively driving improvements and innovation in our workflows and infrastructure. High-agency individual demonstrating initiative, problem-solving, and a commitment to delivering robust and scalable solutions for rapid prototyping and turnaround. We dont need superheroes, just super minds Bachelor's or masters degree in computer science, Engineering, or a related technical field. 5+ years of hands-on experience in a role specifically building and optimizing infrastructure for large-scale machine learning systems Deep practical expertise with AI frameworks (PyTorch, Jax, Pytorch Lightning, etc). Hands-on experience with large-scale multi-node GPU training, and other optimization strategies for developing large foundation models, across various model architectures. Ability to scale solutions involving large datasets and complex models on distributed compute infrastructure. Excellent problem-solving, debugging, and performance optimization skills, with a data-driven approach to identifying and resolving technical challenges. Strong communication and teamwork skills, with a collaborative approach to working with research scientists and other engineers. Experience with MLOps best practices for model tracking, evaluation and deployment. Desired skills Public GitHub profile demonstrating a track record of open-source contributions to relevant projects in data engineering or deep learning infrastructure is a BIG PLUS. Experience with performance monitoring and profiling tools for distributed training and data pipelines. Experience writing CUDA/Triton/CUTLASS kernels.

Job Information Department Name Platforms & Compilers Job Type Full time Date Opened 14/05/2025 Industry Software Development Minimum Experience In Years 3 Maximum Experience In Years 5 City Saidapet Province Tamil Nadu Country India Postal Code 600089 About Us MulticoreWare is a global software solutions & products company with its HQ in San Jose, CA, USA. With worldwide offices, it serves its clients and partners in North America, EMEA and APAC regions. Started by a group of researchers, MulticoreWare has grown to serve its clients and partners on HPC & Cloud computing, GPUs, Multicore & Multithread CPUS, DSPs, FPGAs and a variety of AI hardware accelerators. MulticoreWare was founded by a team of researchers that wanted a better way to program for heterogeneous architectures. With the advent of GPUs and the increasing prevalence of multi-core, multi-architecture platforms, our clients were struggling with the difficulties of using these platforms efficiently. We started as a boot-strapped services company and have since expanded our portfolio to span products and services related to compilers, machine learning, video codecs, image processing and augmented/virtual reality. Our hardware expertise has also expanded with our team; we now employ experts on HPC and Cloud Computing, GPUs, DSPs, FPGAs, and mobile and embedded platforms. We specialize in accelerating software and algorithms, so if your code targets a multi-core, heterogeneous platform, we can help. Job Description Job Summary We are seeking an experienced GPU Programming Engineer to join our team. In this role, you will focus on developing, optimizing, and deploying GPU-accelerated solutions for high-performance machine learning workloads. The ideal candidate has strong expertise in GPU programming across one or more platforms (e.g., NVIDIA CUDA, AMD ROCm/HIP, or OpenCL) and is comfortable working at the intersection of parallel computing, performance tuning, and ML system integration. Key Responsibilities Develop, optimize, and maintain GPU-accelerated components for machine learning pipelines using frameworks such as CUDA, HIP, or OpenCL Analyze and improve GPU kernel performance through profiling, benchmarking, and resource optimization. Optimize memory access, compute throughput, and kernel execution to improve overall system performance on the target GPUs. Port existing CPU-based implementations to GPU platforms while ensuring correctness and performance scalability. Work closely with system architects, software engineers, and domain experts to integrate GPU-accelerated solutions. Required Qualifications Bachelor's or master's degree in computer science, Electrical Engineering, or a related field. 3+ years of hands-on experience in GPU programming using CUDA, HIP, OpenCL, or other GPU compute APIs. Strong understanding of GPU architecture, memory hierarchy, and parallel programming models. Proficiency in C/C++ and hands-on experience developing on Linux-based systems. Familiarity with profiling and tuning tools such as Nsight, rocprof, or Perfetto. Preferred Qualifications Familiarity with cuDNN, TensorRT, OpenCL, or other GPU computing libraries.

Job Summary We are seeking an experienced GPU Programming Engineer to join our team. In this role, you will focus on developing, optimizing, and deploying GPU-accelerated solutions for high-performance machine learning workloads. The ideal candidate has strong expertise in GPU programming across one or more platforms (e.g., NVIDIA CUDA, AMD ROCm/HIP, or OpenCL) and is comfortable working at the intersection of parallel computing, performance tuning, and ML system integration. Key Responsibilities Develop, optimize, and maintain GPU-accelerated components for machine learning pipelines using frameworks such as CUDA, HIP, or OpenCL Analyze and improve GPU kernel performance through profiling, benchmarking, and resource optimization. Optimize memory access, compute throughput, and kernel execution to improve overall system performance on the target GPUs. Port existing CPU-based implementations to GPU platforms while ensuring correctness and performance scalability. Work closely with system architects, software engineers, and domain experts to integrate GPU-accelerated solutions. Required Qualifications Bachelor's or master's degree in computer science, Electrical Engineering, or a related field. 3+ years of hands-on experience in GPU programming using CUDA, HIP, OpenCL, or other GPU compute APIs. Strong understanding of GPU architecture, memory hierarchy, and parallel programming models. Proficiency in C/C++ and hands-on experience developing on Linux-based systems. Familiarity with profiling and tuning tools such as Nsight, rocprof, or Perfetto. Preferred Qualifications Familiarity with cuDNN, TensorRT, OpenCL, or other GPU computing libraries.

About the Role: We are based in Gurgaon and looking for a Senior Computer Vision Engineer to join our team and help our team to improve and create new technologies. You'll work on projects which makes online assessment more secure and cheating proof. If you're a seasoned computer vision expert with a passion for innovation and a track record of delivering impactful solutions, we would be happy to meet you. Role : Senior Computer Vision Engineer Functional Area : AI Educational Qualification: BTech/MS/MTech/PhD in Computer Science/Computer vision/Signal Processing/Deep Learning or equivalent. Should have worked in an academic or professional setting in the field of computer vision/signal processing. Experience: 2-5 years Location : Gurgaon Key Responsibilities: Develop and optimize advanced computer vision algorithms for image and video analysis tasks. Design, implement and train deep learning models for object detection, face processing, activity recognition and other related tasks. Test and refine models and systems based on real-world data and feedback. Evaluate project requirements, plan and manage the roadmap of a project. Present findings and insights in a clear and concise manner to stakeholders. Collaborate and help to integrate and deploy computer vision systems into broader product architecture. Conduct research to stay updated on emerging computer vision technologies and trends. Automate data preprocessing and annotation processes to streamline workflow efficiency. Maintain comprehensive documentation for algorithms, implementations, and evaluations. Mentor junior engineers and provide strategic guidance on project development. Requirements and skills: Proficiency in Python and knowledge of C++, Java and JS is plus. Solid understanding of neural networks, especially convolutional neural networks (CNNs). Knowledge of RCNNs and vision transformers. Proficient in understanding, designing and implementing deep learning models using frameworks such as TensorFlow, PyTorch and Keras. Understanding of fundamental image processing techniques like image filtering, edge detection, image segmentation and image augmentation. Experience in evaluating computer vision models using relevant metrics and performance indicators. Familiarity with GPU and related technologies which is utilized for improved computational efficiency such as CUDA, CUDNN, tensorRT etc. Familiarity with Python libraries such as OpenCV, NumPy, Pandas and scikit-learn etc. Basic knowledge of linear algebra, calculus, and statistics. Strong critical thinking, analytical, and problem-solving skills Self-motivated, quick learner and strong team player with ability to work with minimal supervision.

Stride DynamicsWe are an early-stage Robotics startup developing autonomous aerial robots. We are IIT Kanpur Alumni with extensive experience building autonomous systems for government, defence and enterprises in India and abroad.With Stride Dynamics, we envision leading the next generation of autonomous aerial robots in India and making global standard products for defence, government and enterprises. The RoleWe are looking for someone with a passion for working on hardware and autonomous systems. As a robotics engineer, you will work on our core technology for autonomous flight and contribute from conceptualisation to deployment. You will have the opportunity to work on concepts like localisation, controls, perception, navigation, and planning. We are working on developing aerial vehicles with very robust localisation, enabling them to navigate in any conditions (indoors, dark, dusty, high altitude GNSS jamming scenarios, etc.). The WorkDesign, develop and debug the autonomy software stack for our systems.Work on computer vision, learning based perception, and localisation for aerial systems.A lot of testing in real-world environments.Document and maintain efficient, modular, and reliable C++ code.Develop and improve algorithms for various autonomy modules.Research, understand and implement state-of-the-art methods.. We’re looking for someone withExperience with hardware and implementing algorithms.Experience in C++, Python and ROS.Experience with computer vision, localisation (filtering, PGO, visual odometry).Has Experience with Linux Development Environment and tools like CMake, Git, etc. Bonus if you:Have hands-on experience with robots in the form of projects or competitions.Experience/knowledge of Deep Learning based approaches in Robotics.Experience with GPU/VPU-accelerated programming (eg, CUDA, OpenCL).Published research in the Robotics domain. If you match the above, why usWork in a culture that celebrates innovation, creativity, and the freedom to challenge the status quo.Work with a team of people who are passionate about hardware and robotics.Join us and help us design the future of drones! Apart from the above job description, if you think you can contribute in any other domains (eg, embedded software, hardware, machine learning), feel free to reach out to us.

At Ardee Yantrik, we’re attacking a multi-billion-dollar opportunity—doing things that have never been done before to support an industry struggling with a lack of skilled labor. We solve big, hard problems every day, and our people are our greatest asset in making that happen. Ardee Yantrik is revolutionising manufacturing with advanced robotics and automation solutions. As a Senior Software Developer, you will design and develop high-performance desktop applications for robotic systems and play a vital role in delivering cutting-edge solutions. You will join a team of dedicated, supportive, and enthusiastic people to help create the future of manufacturing. What You’ll DoTech You Will PerformCollaborate with cross-functional teams to design, develop, and optimise desktop applications for advanced manufacturing automation.Develop and optimise desktop applications for advanced manufacturing systems using C++ and Qt.Experience developing CAD based software.Work with 3D rendering and visualisation using OpenGL and related tools.Ensure code quality, modularity, and performance through robust testing, clean coding practices, and version control systems.Utilise modern software development practices, including Agile methodologies, issue tracking, and continuous integration. Who You AreEducation and ExperienceBachelor’s degree in Computer Science, or a related field—or equivalent industry experience.Strong proficiency in C++ with experience in object-oriented programming and design patterns.Expertise in Qt for UI development and OpenGL for 3D rendering.Experience with Git for version control and CMake for build configuration.Excellent knowledge of Data Structures and Algorithms along with strong fundamentals in OOPs concepts.Familiarity with parallelisation techniques (e.g., multithreading, OpenMP, or CUDA) to optimise performance.Proficiency in Jira or similar issue-tracking tools for project management.Experience with Agile development methodologies, including sprint planning, code reviews, and team collaboration.Hands-on experience with unit testing frameworks (e.g., Google Test, Catch2) and debugging tools (e.g., gdb, Valgrind).Knowledge of continuous integration tools such as Jenkins, GitHub Actions, or GitLab CI/CD.Strong understanding of performance tuning, memory management, and debugging complex issues in large-scale applications.Experience in cross-platform development (Windows, Linux).Real-Time Image Processing background, with an understanding of 3D data and computer vision techniques, is highly desirable. Why Join UsImpactful Work: Shape the future of manufacturing with cutting-edge robotics and automation solutions.Innovative Environment: Collaborate with a team that values creativity, experimentation, and taking calculated risks.Career Growth: Opportunities for professional development, mentorship, and leadership. Join a pioneering team where you’ll work on cutting-edge robotics and real-time imaging solutions that push the limits of modern manufacturing. Join Ardee Yantrik and be part of an environment where you’ll innovate, experiment, and make a lasting impact on the manufacturing industry.

Institute of Information Science Postdoctoral Researcher 2 Person The Computer Systems Laboratory - Machine Learning Systems Team Focuses On Research Areas Including Parallel And Distributed Computing, Compilers, And Computer Architecture. We Aim To Leverage Computer System Technologies To Accelerate The Inference And Training Of Deep Learning Models And Develop Optimizations For Next-generation AI Models. Our Research Emphasizes The Following Job DescriptionUnit Institute of Information ScienceJobTitle Postdoctoral Researcher 2 PersonWork Content Research on Optimization of Deep Learning Model Inference and Training AI Model Compression and Optimization Model Compression Techniques (e.g., Pruning And Quantization) Reduce The Size And Computational Demands Of AI Models, Which Are Crucial For Resource-constrained Platforms Such As Embedded Systems And Memory-limited AI Accelerators. We Aim To Explore AI compiler: deployment methods for compressed models across servers, edge devices, and heterogeneous systems. High performance computing: efficient execution of compressed models on processors with advanced AI extensions, e.g., Intel AVX512, ARM SVE, RISC-V RVV, and tensor-level accelerations on GPUs and NPUs. AI Accelerator Design We aim to design AI accelerators for accelerating AI model inference, focusing on software and hardware co-design and co-optimization. Optimization of AI Model Inference in Heterogeneous Environments Computer Architectures Are Evolving Toward Heterogeneous Multi-processor Designs (e.g., CPUs + GPUs + AI Accelerators). Integrating Heterogeneous Processors To Execute Complex Models (e.g., Hybrid Models, Multi-models, And Multi-task Models) With High Computational Efficiency Poses a Critical Challenge. We Aim To Explore Efficient scheduling algorithms. Parallel algorithms for the three dimensions: data parallelism, model parallelism, and tensor parallelism. Qualifications Ph.D. degree in Computer Science, Computer Engineering, or Electrical Engineering Experience in parallel computing and parallel programming (CUDA or OpenCL, C/C++ programming) or hardware design (Verilog or HLS) Proficient in system and software development Candidates With The Following Experience Will Be Given Priority Experience in deep learning platforms, including PyTorch, TensorFlow, TVM, etc. Experience in high-performance computing or embedded systems. Experience in algorithm designs. Knowledge of compilers or computer architectureWorking EnvironmentOperating Hours 8:30AM-5:30PMWork Place Institute of Information Science, Academia SinicaTreatment According to Academia Sinica standards: Postdoctoral Researchers: NT$64,711-99,317/month. Benefits include: labor and healthcare insurance, and year-end bonuses. Reference Site 洪鼎詠網頁: http://www.iis.sinica.edu.tw/pages/dyhong/index_zh.html, 吳真貞網頁: http://www.iis.sinica.edu.tw/pages/wuj/index_zh.html Please Email Your CV (including Publications, Projects, And Work Experience), Transcripts (undergraduate And Above), And Any Other Materials That May Assist In The Review Process To The Following PIs Acceptance MethodContacts Dr. Ding-Yong Hong Contact Address Room 818, New IIS Building, Academia Sinica Contact Telephone 02-27883799 ext. 1818Email dyhong@iis.sinica.edu.tw Required Documents Dr. Ding-Yong Hong: dyhong@iis.sinica.edu.tw Dr. Jan-Jan Wu: wuj@iis.sinica.edu.twPrecautions for application DatePublication Date 2025-01-20Expiration Date 2025-12-31

Job Title: AI ManagerLocation: Chennai (Hybrid role – 3 days per week onsite. Candidates must be willing to relocate to Chennai) Key Responsibilities: • Lead and mentor a team of algorithm engineers, providing guidance and support to ensure their professional growth and success. • Develop and maintain the infrastructure required for the deployment and execution of algorithms at scale. • Collaborate with data scientists, software engineers, and product managers to design and implement robust and scalable algorithmic solutions. • Optimize algorithm performance and resource utilization to meet business objectives. • Stay up-to-date with the latest advancements in algorithm engineering and infrastructure technologies, and apply them to improve our systems. • Drive continuous improvement in development processes, tools, and methodologies. Qualifications: • Bachelor's or Master's degree in Computer Science, Engineering, or a related field. • Proven experience in developing computer vision and image processing algorithm and ML/DL algorithm. • Familiar with high performance computing, parallel programming and distributed systems. • Strong leadership and team management skills, with a track record of successfully leading engineering teams. • Proficiency in programming languages such as Python, C++ and CUDA. • Excellent problem-solving and analytical skills. • Strong communication and collaboration abilities. Preferred Qualifications: • Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn). • Experience with GPU architecture and algo development toolkit like Docker, Apptainer. Minimum Must Have's:• Bachelor's degree plus 8 + years of experience • Master's degree plus 8 + years of experience • Familiar with high performance computing, parallel programming and distributed systems. You can also share your resume at hr@alphasqmax.com

Job Area: Engineering Group, Engineering Group > Systems Engineering General Summary: Do you ever wonder when will connected devices become thinking devices? Be part of the group that is working on technology that will bring "Cognition" to all connected devicesThat means devices that don"™t just think but instinctively react to their surroundings. We are searching for an AI Systems Architect Engineer to be part of the Qualcomm AI Processor team responsible for developing DSP and Machine Learning software applications and use cases developed for Qualcomm Snapdragon processors. The candidate will work on modelling and analysis of new cutting-edge algorithms in the areas of machine learning, computer vision and video processing that bring artificial intelligence to mobile and edge devices. Responsibilities include analyzing and optimizing custom processors/accelerators, developing and training data-driven architecture models, correlating these models, and performing system-level architecture analysis. Minimum Qualifications: Experienced candidates (1 - 4 years) are welcome to apply with experience in the following area: Strong academic records (GPA 3.0 or 72% and better) Excellent programming skills in C/C++, Python Strong problem-solving skills Strong motivation and capabilities in learning new subjects especially in the field of artificial intelligence Knowledge of data-driven modelling Knowledge of computer and hardware architecture Effective interpersonal communications skill (written and verbal) Analytical, thorough, resourceful, and detail-oriented Self-motivated, hardworking, and flexible Preferred Qualifications: Basic understanding of machine learning, computer vision, and digital image processing algorithms and applications Advanced understanding of computer architecture Advanced understanding of data-driven modelling Excellent verbal, written, and presentation skills Ability to work effectively as part of a team Knowledge of OOP principles Knowledge of GPU Programming / Architecture is a bonus Minimum Education Required : Masters/Bachelor"™s Computer Engineering, Electrical Engineering or Engineering Science Minimum Qualifications: Bachelor's degree in Engineering, Information Systems, Computer Science, or related field.

Develop and optimize HPC applications and algorithms using CUDA, MPI, OpenMP on Azure and cluster systems. Support scientific teams by modernizing codebases and enabling GPU acceleration. Required Candidate profile Software engineer with 5+ years in HPC programming, scientific code optimization, GPU computing, and collaboration with research teams.

A Moving Experience. Do you have a passion for pushing the boundaries of innovation? Are you excited about AI’s potential to improve the human experience? Then come join the ride! Who is Cerence ai? Cerence ai is the global leader in AI for transportation, specialized in building AI and voice-powered companions for cars, two-wheelers, and more that enable people to focus on what matters most. With over 500 million cars shipped with Cerence ai technology, we partner with leading automakers (such as Volkswagen, Mercedes, Audi, Toyota and many more), mobility providers, and technology companies to power intuitive, integrated experiences that create safer, more connected, and more enjoyable journeys for drivers and passengers alike. Our Driving Force Our team is dedicated to pushing the boundaries of AI innovation, working around the globe with headquarters in Burlington, Massachusetts, USA and 16 other offices across Europe, Asia, and North America. We bring together diverse backgrounds and varied skill sets with the shared goal of advancing the next generation of transportation user experiences. Our culture is customer-centric, collaborative, fast-paced, and fun, with continuous opportunities for learning and development to support your career growth. Interested in having a significant impact in a dynamic industry with a high-performing global team? We are seeking a highly skilled and innovative Senior Software Engineer in the TTS (Text-To-Speech) R&D team to develop TTS products exploiting AI models. The ideal candidate will have a robust background (experience or knowledge) in hands on Python programming and ML Ops. Person should be well-versed in the software engineering practices, version controls such as Git, experience and familiar in deployment, optimization of ai model (throughput, efficiency) and with ML-Ops, not for development of AI model but should expert for integration and optimization = productization. Your Impact: Transitioning AI models from research to product-grade and integrating them into observable services optimized for GPUs, specifically for Text-to-Speech (TTS) applications.Develop GPU-accelerated versions of advanced speech AI algorithms like TTS, voice style transfer and neural network-based vocoders etc.Identify and address performance bottlenecks, applying optimization techniques to enhance efficiency.Work collaboratively with cross-functional teams to introduce new product features and enhance existing products What You Bring: Masters or Bachelors (or equivalent experience) in Computer Science, computer architecture, or related field5+ years of experience, excellent Python programming and software design skills, including debugging, performance analysis, and test designExperience with inference Services and productization of AI Models (ML Ops)Excellent Debugging abilities spanning multiple software (storage systems, kernels and containers). Familiarity with version control and code review toolsBackground with container technologies such as docker (Preferred)Background with AI models for Speech Recognition, speech synthesis, Speech Translation, Machine Translation, TTS (Preferred)Experience with Nvidia frameworks and tools for performance measurement and optimization (Preferred)Knowledge of GPU programming such as CUDA or OpenCL (preferred) What We Offer We offer a generous compensation and benefits package (in addition to the base salary), including: Annual bonus opportunityInsurance coverage as per policyPaid time offPaid holidaysCompany contribution to the RRSP (Registered Retirement Savings Plan)Equity awards for certain positions and levelsHybrid work mode is applicable for this position. All compensation and benefits are subject to the terms and conditions of the underlying plans or programs, as applicable, and may be amended, terminated, or replaced from time to time. Cerence Inc. (Nasdaq: CRNC and www.cerence.com) is the global industry leader in creating unique, moving experiences for the automotive world. Spun out from Nuance in October 2019, Cerence is a new, independent company that has quickly gained traction as a leader in the automotive voice assistant space, working with all of the world’s leading automakers – from Ford and Fiat Chrysler to Daimler, Audi and BMW to Geely and SAIC – to transform how a car feels, responds and learns. Its track record is built on more than 20 years of industry experience and leadership and more than 500 million cars on the road today across more than 70 languages. As Cerence looks to the future and continues an ambitious growth agenda, we need someone to join the team and help build the future of voice and AI in cars. This is an exciting opportunity to join Cerence’s passionate, dedicated, global team and be a part of meaningful innovation in a rapidly growing industry. EQUAL OPPORTUNITY EMPLOYER Cerence is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal, state and local laws that prohibit employment discrimination on the basis of age, race, color, gender, gender identity, gender expression, sex, sex stereotyping, pregnancy, national origin, ancestry, religion, physical or mental disability, medical condition, marital status, citizenship status, sexual orientation, protected military or veteran status, genetic information and other protected classifications. Cerence Equal Employment Opportunity Policy Statement. All prospective and current Employees need to remain vigilant when it comes to executing security policies in the workplace. This includes: Following workplace security protocols and training programs to familiarize with the ways to maintain a safe workplace. Following security procedures to report any suspicious activity. Having respect for corporate security procedures to allow those procedures to be effective. Adhering to company's compliance and regulations. Encouraging to follow a zero tolerance for workplace violence. Basic knowledge of information security and data privacy requirements (e.g., how to protect data & how to be handling this data). Demonstrative knowledge of information security through internal training programs.

Dear Candidate, We are hiring a Computer Vision Engineer to develop AI-driven solutions for image recognition, object detection, and video analysis. The role requires expertise in deep learning, computer vision algorithms, and real-time processing techniques. Key Responsibilities: Develop and optimize computer vision models using OpenCV, TensorFlow, and PyTorch. Implement object detection, segmentation, and facial recognition algorithms. Process and analyze large-scale image and video datasets. Optimize deep learning models for real-time inference on edge devices. Collaborate with AI and software teams to integrate vision solutions into applications. Required Skills & Qualifications: Computer Vision Frameworks: OpenCV, DLIB, MediaPipe Deep Learning: TensorFlow, PyTorch, Keras Algorithms: CNNs, YOLO, Faster R-CNN, Mask R-CNN Programming: Python, C++, CUDA Edge AI: TensorRT, OpenVINO, NVIDIA Jetson Experience with autonomous systems, OCR, and SLAM is a plus. Soft Skills: Strong troubleshooting and problem-solving skills. Ability to work independently and in a team. Excellent communication and documentation skills. Note: If interested, please share your updated resume and preferred time for a discussion. If shortlisted, our HR team will contact you. Kandi Srinivasa Reddy Delivery Manager Integra Technologies

ability to write efficient, clean runtime code for applications varying from Quick Prototyping to Complex, Mission-Critical Desktop ApplicationAbility to lead strategies with tools Required Candidate profile Experience in coding with C++, OpenGL and CUDA. Collaboratively influence the Software Development Life Cycle processes changes and best practices. Knowledge of efficient Version Control is required.

Design, develop, and optimize Speech AI models in various tasks including Speech Recognition, Language Identification, Speaker Verification, and Speaker Diarization. Implement and fine-tune Conformer, Transformer, and other state-of-the-art neural network architectures for speech processing. Work with Python3, PyTorch, ONNX, and other related technologies for model building and deployment. Debug and troubleshoot issues in Torch, TensorFlow, and speech AI model servers. Optimize and deploy models using ONNX and other optimization tools to ensure high performance and scalability. Follow standard best practices for modular and clean code. Collaborate with cross-functional teams to integrate models into production environments. Work with version control systems like Git and utilize containerization for smooth deployment.

Job Description As a Software Engineering intern at Intel, you'll bring your formal education and budding on-the-job experience to support and innovate within our software engineering landscape. You'll be immersed in learning the fundamentals of a wide array of technical software skills.Your role is crucial in helping Intel end customers deploying Intel AI accelerator solutions You will engage in a variety of tasks including automation development for AI workloads, AI work load performance benchmarking, developing AI based application pipelines all the while developing your programming and scripting abilities. Join us to put your coding expertise into action, contributing to the software ecosystem that enables the groundbreaking features and functions of Intel's innovative AI Data Center solutionsThe ideal candidate will demonstrate or be developing skills in the following areas: AI algorithm performance profiling and debug using various tools Scripting, automation of work load performance benchmark suites Enabling and porting latest models to Intel AI accelerators Code Review Qualifications Qualifications:Must Have: 1. M.S./ M.Tech/M.E student with deep focus on core Computer Science foundations (data structures, operating system)2. Strong acumen on programming skills in C, C++, Python3. Automation solution development using python, shell etc Good to have 1. Knowledge of Computer Hardware architectures, digital logic etc2. Strong in mathematical foundations, matrix operations : Eigen Values/Eigen Vectors, Simple matrix operations etc3. Exposure to AI concepts and familiarity with Deep learning models4. Exposure to hands on Deep learning using Pytorch, Tensorflow, CuDA etc

- 2+ years of overall experience with large portion of that working on C++ based projects - Hands on implementation of algorithms in Cuda, Shaders on GPU. - Experience in ARM architecture - Very good Knowledge on Object-Oriented Design & System Integration - Very good knowledge on Code Optimization, Implement & Adapt Complex Algorithms - As a member of the team, you will play a critical role in all stages of GPU development - Design and architect features in compute and graphics stimulus development framework similar to OpenGL and CUDA - Strong C++ programming capability required - Graphics or CUDA knowledge a plus - Experience with OpenGL, Vulkan, Direct3D, CUDA APIs a plus Skills: Candidates should have a B.E. or B.Tech. degree in Computer Science, Information Technology or related subjects within the past 5 years.

Job Overview Responsible to drive solutioning for GPU-as-a-Service (GaaS) and AI Cloud offerings. The ideal candidate will design, optimize, and deliver scalable GPU-based cloud solutions leveraging NVIDIA and other AI cloud platforms. Responsibilities Architect and solution GPU-accelerated workloads for AI, ML, and HPC applications. Design and implement scalable GPU-as-a-Service offerings on NVIDIA AI Enterprise, DGX Cloud, or public/private cloud platforms. Collaborate with product, engineering, and sales teams to define AI cloud strategies and customer solutions. Benchmark GPU performance, optimize costs, and ensure seamless cloud integration. Engage with clients to understand workloads, recommend architectures, and support deployments. Educational Qualifications BE/B-Tech or equivalent with Computer Science or Electronics & Communication RELEVANT EXPERIENCE Relevant Experience in AI Cloud, GPU computing, or solution architecture. Hands-on experience with NVIDIA AI, DGX systems, CUDA, Triton Inference Server, and cloud platforms (AWS, Azure, GCP). Strong understanding of AI/ML pipelines, Kubernetes, and containerization. Excellent communication and pre-sales solutioning skills.

About Toyota ConnectedIf you want to change the way the world works, transform the automotive industry and positively impact others on a global scale, then Toyota Connected is the right place for you! Within our collaborative, fast-paced environment we focus on continual improvement and work in a highly iterative way to deliver exceptional value in the form of connected products and services that wow and delight our customers and the world around us. About the TeamToyota Connected India is looking for an experienced Lead Computer Vision Engineer to drive the development of cutting-edge AI-powered applications for Software-Defined Vehicles (SDVs). In this role, you will lead a team of engineers and researchers in building real-time perception, autonomous decision-making, and interactive AI systems that enhance in-vehicle intelligence, driver assistance, and passenger experiences.This is an exciting opportunity to work at the intersection of Generative AI, Computer Vision, and Automotive Technology, shaping the future of mobility with AI-driven capabilities. What you will doTechnical Leadership: Lead the design, development, and deployment of Generative AI and Computer Vision models for SDV applications, including driver monitoring, object detection, autonomous navigation, and in-vehicle user experience.Team Management: Mentor and guide a team of AI engineers, fostering a culture of innovation, collaboration, and technical excellence.AI Model Development: Architect and train deep learning models for real-time perception, scene understanding, and multimodal AI applications (vision, NLP, speech).Integration with SDVs: Work closely with ADAS (Advanced Driver Assistance Systems), IoT, and embedded software teams to integrate AI-powered solutions into vehicle architectures.Real-Time Edge AI Deployment: Optimize AI models for real-time processing on automotive-grade edge hardware (NVIDIA, Qualcomm Snapdragon or similar).Multimodal AI Systems: Develop AI-powered human-vehicle interaction systems, including Drive Monitoring, and predictive user interfaces.Research & Innovation: Stay ahead of the latest advancements in AI/ML, diffusion models, and self-supervised learning to push the boundaries of automotive AI applications.Collaboration & Partnerships: Work with OEMs, Tier 1 suppliers, and research institutions to align AI innovations with industry needs. You are a successful candidate if you haveMaster’s or Ph.D. in Computer Science, AI, Machine Learning, Computer Vision, or a related field.10+ years of experience in Computer Vision, Deep Learning, or Generative AI.5+ years of experience leading AI engineering teams.Proven track record of developing and deploying AI models in real-world applications (preferably automotive or robotics).Technical Skills:Strong expertise in Deep Learning frameworks (TensorFlow, PyTorch, JAX).Experience with Generative AI models (Stable Diffusion, GANs, NeRFs, or similar).Proficiency in 3D Computer Vision (SLAM, depth estimation, sensor fusion).Experience with transformer-based architectures, and multimodal AI.Hands-on experience with embedded AI/Edge ML (NVIDIA Jetson, Qualcomm AI SDKs, TensorRT, OpenVINO).Strong programming skills in Python, C++, CUDA, and ROS.Experience with automotive data formats (CAN, ROS, OpenDRIVE, ASAM OpenSCENARIO).Industry Knowledge: Understanding of ADAS, autonomous driving, digital twins, and SDV architectures Preferred Qualifications:Experience in AI-driven Digital Cockpit and next-gen HMI (Human-Machine Interface) applications.Familiarity with 3D synthetic data generation for training AI models.Strong understanding of real-time sensor fusion (LIDAR, CAN, DMS, IMU).Experience working with Automotive OEMs and Tier 1 suppliers.Experience with cloud-based AI training and edge-to-cloud inference architectures. What is in it for you?Top of the line compensation!You'll be treated like the professional we know you are and left to manage your own time and workload.Yearly gym membership reimbursement & Free catered lunches.No dress code! We trust you are responsible enough to choose what’s appropriate to wear for the day.Opportunity to build products that improves the safety and convenience of millions of customersCool office space and other awesome benefits! Our Core Values: EPICEmpathetic: We begin making decisions by looking at the world from the perspective of our customers, teammates, and partners.Passionate: We are here to build something great, not just for the money. We are always looking to improve the experience of our millions of customersInnovative: We experiment with ideas to get to the best solution. Any constraint is a challenge, and we love looking for creative ways to solve them.Collaborative: When it comes to people, we think the whole is greater than its parts and that everyone has a role to play in the success! To know more about us ,check out our glassdoor page-https://www.glassdoor.co.in/Reviews/TOYOTA-Connected-Corporation-Reviews-E3305334.htm

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.

Core Responsibilities: 1. Strategic AI/ML Architecture & Vision Architectural Roadmap : Develop a comprehensive AI/ML architecture roadmap aligned with the company's strategic goals. Identify the right technologies, tools, and frameworks needed to build and scale AI-powered manufacturing automation solutions. Platform Strategy : Define the platform strategy for AI/ML deployment in manufacturing environments, with a focus on scalability, modularity, and performance. Establish guidelines for developing reusable components, libraries, and APIs to support diverse AI applications. Thought Leadership : Act as an internal and external AI thought leader, representing the company's AI initiatives in conferences, industry panels, and client meetings. Advocate for best practices in AI/ML architecture and contribute to the company's knowledge base. 2. AI/ML Solution Design & Development End-to-End Solution Design : Lead the design and development of end-to-end AI solutions for manufacturing, from data ingestion and processing to model deployment and inference. Ensure that solutions are architected to meet performance, scalability, and reliability requirements. Model Selection & Optimization : Guide the team in selecting appropriate machine learning and deep learning models based on project requirements. Optimize model performance through hyperparameter tuning, model compression, and deployment strategies. Generative AI Integration : Research and integrate generative AI models for use cases such as predictive maintenance, defect detection, and process optimization. Explore innovative applications of generative AI within manufacturing to enhance automation and provide data-driven insights. CUDA Optimization for Deep Learning : Leverage CUDA and GPU acceleration to optimize the training and inference of deep learning models, ensuring high-performance execution for large datasets and real-time manufacturing applications. 3. Infrastructure and Deployment Containerization with Docker : Design and implement containerized AI/ML solutions using Docker to ensure consistency, scalability, and ease of deployment across cloud and edge environments. Establish best practices for container orchestration and resource management. Cloud and Edge Deployment : Develop strategies for deploying AI models on cloud platforms (e.g., AWS, Azure, GCP) and edge devices to support real-time processing in manufacturing environments. Utilize containerization and orchestration tools like Kubernetes for scalable, multi-environment deployments. Data Pipeline Architecture : Design data pipelines that facilitate real-time processing, storage, and retrieval for AI models. Architect data lakes, warehouses, and streaming frameworks to handle high-volume, high-velocity data in manufacturing environments. 4. Cross-Functional Collaboration & Leadership Collaborate with Engineering and Product Teams : Work closely with software engineering, data science, robotics, and hardware teams to ensure seamless integration of AI components. Provide architectural guidance and support to align efforts across teams. Technical Leadership & Mentorship : Mentor AI/ML engineers, data scientists, and junior architects on best practices in AI architecture, model development, and deployment. Foster a culture of innovation, technical excellence, and collaboration within the team. Stakeholder Engagement : Engage with stakeholders, including product managers, clients, and senior leadership, to understand business requirements and translate them into technical specifications for AI/ML solutions. 5. DevOps, MLOps, and Agile Practices Establish MLOps Pipelines : Design and implement robust MLOps practices to automate model training, testing, deployment, and monitoring. Leverage CI/CD pipelines to streamline the AI/ML lifecycle, ensuring consistent, repeatable results in production. Agile Methodologies : Promote Agile methodologies within the AI/ML team to enhance adaptability and responsiveness. Lead sprint planning, retrospective sessions, and other Agile practices to ensure efficient project execution. DevOps Integration : Collaborate with the DevOps team to integrate AI/ML models into production environments seamlessly. Develop strategies for deploying models on edge devices and cloud platforms, with a focus on high availability and low latency. 6. Performance, Security, and Compliance Performance Tuning : Oversee the optimization of AI/ML algorithms and models to meet the real-time performance requirements of manufacturing automation systems. Utilize techniques such as model pruning, quantization, and distributed processing. Security and Privacy : Ensure that AI systems adhere to best practices in security and data privacy, particularly when handling sensitive manufacturing data. Implement measures to protect against adversarial attacks and data breaches. Compliance and Standardization : Align AI architecture with industry standards, regulatory requirements, and compliance guidelines. Maintain documentation and standards that support reproducibility, traceability, and auditability of AI solutions. Required Qualifications: Education : Master's or Ph.D. in Computer Science, Data Science, Engineering, or a related field with a focus on AI/ML. Experience : 12+ years of experience in AI/ML, with at least 5 years in an architectural or senior technical leadership role. Proven track record of architecting and deploying AI/ML solutions, preferably within manufacturing, industrial automation, or a similar domain. Hands-on experience with a broad range of machine learning, deep learning, and data processing frameworks (e.g., TensorFlow, Keras, PyTorch, Apache Spark). Experience with ML tools and libraries such as scikit-learn, XGBoost, LightGBM, and Hugging Face Transformers. Technical Skills : AI/ML Expertise : Deep understanding of supervised, unsupervised, reinforcement, and generative learning techniques, as well as expertise in model evaluation, tuning, and optimization. CUDA and GPU Processing : Proficiency in GPU acceleration using CUDA for model training and inference optimization. Data Engineering : Proficiency in data pipeline design, big data processing, and storage solutions (e.g., Kafka, Hadoop, Snowflake). Cloud and Edge Deployment : Experience deploying AI models on cloud (e.g., AWS, Azure, GCP) and edge computing platforms. Understanding of distributed computing and containerization (Docker, Kubernetes). Programming Skills : Strong programming skills in Python, along with experience in Java, C++, or other relevant languages for AI and data processing. MLOps and DevOps : Proficiency in MLOps practices and tools (e.g., MLflow, Kubeflow, DVC) for model versioning, experiment tracking, and automated deployment. Experience with CI/CD pipelines and DevOps practices. Model Monitoring and Maintenance : Experience with tools and practices for monitoring model performance in production, detecting drift, and implementing automated retraining pipelines. A/B Testing and Experimentation : Familiarity with designing and implementing A/B testing frameworks for AI/ML models to evaluate performance improvements and new features. Scalability and Performance Optimization : Advanced knowledge of techniques for scaling AI/ML systems to handle large-scale data and high-throughput requirements in manufacturing environments. Ethical AI and Bias Mitigation : Understanding of ethical considerations in AI development and deployment, including techniques for identifying and mitigating bias in models and datasets. Regulatory Compliance : Knowledge of AI-specific regulations and standards relevant to the manufacturing industry, and experience in implementing compliant AI systems. Open Source Contributions and Management : Experience contributing to and managing open-source AI/ML projects, understanding of open-source licensing, and ability to evaluate open-source tools for integration into the company's AI stack. Security in AI Systems : In-depth knowledge of security best practices for AI/ML systems, including: Techniques for securing model training and inference pipelines Methods to protect against model inversion and membership inference attacks Strategies for secure model deployment and updates in production environments AI-specific Security Frameworks : Familiarity with AI-specific security frameworks and guidelines, such as those provided by NIST or other industry-standard organizations. Secure Data Handling : Expertise in implementing secure data handling practices throughout the AI lifecycle, including data collection, storage, processing, and deletion, in compliance with data protection regulations. Industry-Specific Knowledge : Familiarity with manufacturing processes, automation systems, and Industry 4.0 concepts to better apply AI/ML solutions in the manufacturing context. Risk Management : Experience in identifying and mitigating risks associated with AI implementation in critical manufacturing environments. Explainable AI (XAI) : Knowledge of techniques for making AI models more interpretable and transparent, which is crucial for regulatory compliance and stakeholder trust. Cross-Functional Communication : Strong ability to communicate complex AI concepts to non-technical stakeholders, including executives, clients, and regulatory bodies. Continuous Learning : Demonstrated commitment to staying updated with the latest advancements in AI/ML, particularly those relevant to manufacturing and automation. Software Architecture : Strong understanding of software design patterns, microservices architecture, and API design principles. Database Technologies : Experience with both SQL and NoSQL databases, including designing schemas for efficient data storage and retrieval in AI applications. Data Visualization : Familiarity with data visualization libraries and tools (e.g., Matplotlib, Seaborn, Plotly, Tableau) for effectively communicating insights from AI models.

Job Title: Senior GPU Engineer Job Location: Hyderabad, India Job Summary: Right Skale is seeking a GPU engineer to develop and optimize algorithms for real-time medical ultrasound imaging. This position is with an ultrasound company located in the heart of Silicon Valley, USA. The candidate shall be well versed in modern GPU architectures, frameworks, and languages, adhere to software development lifecycle best practices and be comfortable working from requirements and detail design documents to production code. The position will also have an opportunity to work directly with the client at their US location. Job Responsibilities: Development of GPU-optimized signal and image processing pipelines in collaboration with a cross-functional team of experts, engineers and designers. Development of automated test procedures and infrastructure of the pipelines. Design reviews, code reviews, verification testing and analysis of the mobile application. Ownership of particular imaging modes and features such as advanced image. processing, 3D visualization, A.I. assisted detection, etc. Education/Experience Requirements: 4-5 years relevant experience in a software development role including experience with mobile OSs. Demonstrated abilities of implementing realtime signal processing algorithms on a GPU. Ultrasound and Medical Imaging background is a plus. Solid programming skills in iOS (Swift), Android (Java/C++), Python and familiarity with GPU frameworks/SDKs such as Metal2, CUDA, OpenGL, OpenCL, or Vulkan.

NP : Immediate to 45 days Job Description : We are looking for strong C/C++ developers with a passion for performance optimization and systems programming. While prior experience with CUDA, OpenCL, or hardware accelerators is a plus, we welcome candidates who are eager to learn and scale up in machine learning, computer vision, and numeric library optimization for CPUs, GPUs, DSPs, and accelerators. Key Responsibilities : - Develop and optimize high-performance software using C/C++ for numerical computing, machine learning, and computer vision applications. - Learn and apply low-level optimizations, including parallelization, vectorization, and memory management, to enhance execution on hardware platforms. - Work closely with software and hardware engineers to adapt algorithms for maximum efficiency on target architectures. - Gain hands-on experience with CUDA, OpenCL, or similar programming models as part of the role. - Collaborate with customers to understand their requirements and develop tailored software solutions. - Conduct performance analysis and benchmarking to ensure optimized execution. - Stay up to date with the latest advancements in hardware acceleration and high-performance computing. Qualifications : - BTech/BE/MTech/ME/MS/PhD in CSE/IT/ECE. - 2+ years of experience in C/C++ development, with a strong grasp of data structures, algorithms, and performance optimization. - Willingness to learn and grow in GPU programming, parallel computing, and hardware acceleration. - Knowledge of parallel computing concepts, SIMD instructions, and memory hierarchies is a plus. - Prior experience with CUDA, OpenCL, or similar is advantageous but not mandatory. - Strong problem-solving skills and the ability to work independently or in a team.

Mandatory skills : C/C++, CUDA, OpenCL, or other relevant languages for hardware optimization We are seeking a talented engineer to implement and optimize machine learning, computer vision, and numeric libraries for target hardware architecture, including CPUs, GPUs, DSPs, and other accelerators. Your expertise will be instrumental in enabling efficient and high- performance execution of algorithms on these hardware platforms. Key Responsibilities : - Implement and optimize machine learning, computer vision, and numeric libraries for target hardware architectures, including CPUs, GPUs, DSPs, and other accelerators. - Work closely with software and hardware engineers to ensure optimal performance on target platforms. - Implement low- level optimizations, including algorithmic modifications, parallelization, vectorization, and memory access optimizations, to fully leverage the capabilities of the target hardware architectures. - Work with customers to understand their requirements and implement libraries to meet their needs. - Develop performance benchmarks and conduct performance analysis to ensure the optimized libraries meet the required performance targets. - Stay current with the latest advancements in machine learning, computer vision, and high- performance computing. Qualifications : - BTech/BE/MTech/ME/MS/PhD degree in CSE/IT/ECE - 5+ years of experience working in Algorithm Development, Porting, Optimization & Testing - Proficient in programming languages such as C/C++, CUDA, OpenCL, or other relevant languages for hardware optimization. - Hands- on experience with hardware architectures, including CPUs, GPUs, DSPs, and accelerators, and familiarity with their programming models and optimization techniques. - Knowledge of parallel computing, SIMD instructions, memory hierarchies, and cache optimization techniques. - Experience with performance analysis tools and methodologies for profiling and optimization. - Knowledge of deep learning frameworks and techniques is good to have - Strong problem- solving skills and ability to work independently or within a team.

Design, develop, and optimize HPC applications, cloud-based solutions, and infrastructure for compute-intensive workloads. Manage HPC clusters, AI/ML integration, and parallel computing. Ensure performance tuning, and drive cloud-HPC innovations. Required Candidate profile HPC Engineer with 5-10 years Exp in cloud and HPC solutions, expertise in AI/ML, GPU programming (CUDA, OpenMP, MPI), Azure, Linux, storage, networking, and performance tuning.

We are seeking a talented engineer to implement and optimize machine learning, computer vision, and numeric libraries for target hardware architecture, including CPUs, GPUs, DSPs, and other accelerators. Your expertise will be instrumental in enabling efficient and high-performance execution of algorithms on these hardware platforms.Key Responsibilities: Implement and optimize machine learning, computer vision, and numeric libraries for target hardware architectures, including CPUs, GPUs, DSPs, and other accelerators. Work closely with software and hardware engineers to ensure optimal performance on target platforms. Implement low-level optimizations, including algorithmic modifications, parallelization, vectorization, and memory access optimizations, to fully leverage the capabilities of the target hardware architectures. Work with customers to understand their requirements and implement libraries to meet their needs. Develop performance benchmarks and conduct performance analysis to ensure the optimized libraries meet the required performance targets. Stay current with the latest advancements in machine learning, computer vision, and high-performance computing. Qualifications: BTech/BE/MTech/ME/MS/PhD degree in CSE/IT/ECE 2 years of experience working in Algorithm Development, Porting, Optimization & Testing Proficient in programming languages such as C/C++, CUDA, OpenCL, or other relevant languages for hardware optimization. Hands-on experience with hardware architectures, including CPUs, GPUs, DSPs, and accelerators, and familiarity with their programming models and optimization techniques. Knowledge of parallel computing, SIMD instructions, memory hierarchies, and cache optimization techniques. Experience with performance analysis tools and methodologies for profiling and optimization. Knowledge of deep learning frameworks and techniques is good to have Strong problem-solving skills and ability to work independently or within a team.