4 Triton Jobs

Job Title: Accelerated Computing Engineer Entry Level Experience Level: 02 Years Location: Vellore Employment Type: Full-time About the Role We seek a driven Accelerated Computing Engineer to join our innovative team in Vellore. This entry-level role offers a unique opportunity to work with advanced AI/ML models, accelerated computing technologies, and cloud infrastructure while collaborating on cutting-edge research and deployment projects. You will work with a variety of state-of-the-art models such as BGE-Large, Mixtral, Gemma, LLaMA, and Stable Diffusion, as well as other fine-tuned architectures, to solve real-world computing challenges through advanced AI/ML infrastructure solutions. Key Responsibilities Customer Interaction & Analysis: Work closely with customers to analyze technical and business needs, translating them into robust, AI-driven solutions. Model Deployment & Optimization: Develop and deploy advanced AI/ML models such as LLaMA, Mixtral, Gemma, and other GenAI models while optimizing their performance for varied computing environments. Performance Testing & System Benchmarking: Execute advanced test scenarios and performance benchmarks across AI/ML models and distributed systems to ensure optimal performance. Infrastructure & Model Research: Research, configure, and maintain infrastructure solutions (using tools like TensorRT and PyTorch) supporting our models and accelerated computing workloads. AI/ML Model Integration: Support and deploy models such as Stable Diffusion, BGE, Mistral, and custom fine-tuned models into end-to-end pipelines for AI/ML-driven solutions. Automation & Process Improvements: Drive automation strategies to streamline workflows, improve testing accuracy, and optimize system performance. Technical Liaison: Served as the technical bridge by collaborating with product development teams, tracking customer feedback, and ensuring timely resolutions. Model Configuration & Troubleshooting: Create custom scripts, troubleshoot advanced configurations, and support tuning efforts for AI/ML model customization. Skills & Qualifications Required Skills: Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical discipline. Strong foundational knowledge of AI/ML model deployment and cloud infrastructure. Proficiency with AI/ML frameworks & libraries, including PyTorch, TensorRT, and Triton. Hands-on experience with deployment models such as LLaMA, Mixtral, Gemma, and Stable Diffusion. Familiarity with distributed computing environments and orchestration tools like Kubernetes. Proficiency in workflow automation, performance tuning, and large-scale system debugging. Understanding of cloud computing technologies and infrastructure architecture, including storage, networking, and computing paradigms. Preferred Skills: Experience working with object storage technologies like AWS S3, Azure Blob Storage, and MinIO. Familiarity with advanced AI/ML model frameworks such as Gemma-2b, Mixtral-8x7b, Mistral-7b-instruct, and other fine-tuned AI models. Expertise in GPU configuration and tuning for AI/ML workloads, including drivers and machine learning optimization strategies. Familiarity with serverless computing and Function as a Service (FaaS) concepts. Experience with infrastructure as code (IaC) and performance benchmarking methodologies.

Looking for challenging roleIf 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. 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 Type: Full Time Job Description: We are seeking an experienced AI Engineer with 4-5 years of hands-on experience in designing and implementing AI solutions. The ideal candidate should have a strong foundation in developing AI/ML-based solutions, including expertise in Computer Vision (OpenCV). Additionally, proficiency in developing, fine-tuning, and deploying Large Language Models (LLMs) is essential. As an AI Engineer, candidate will work on cutting-edge AI applications, using LLMs like GPT, LLaMA, or custom fine-tuned models to build intelligent, scalable, and impactful solutions. candidate will collaborate closely with Product, Data Science, and Engineering teams to define, develop, and optimize AI/ML models for real-world business applications. Key Responsibilities: Research, design, and develop AI/ML solutions for real-world business applications, RAG is must. Collaborate with Product & Data Science teams to define core AI/ML platform features. Analyze business requirements and identify pre-trained models that align with use cases. Work with multi-agent AI frameworks like LangChain, LangGraph, and LlamaIndex. Train and fine-tune LLMs (GPT, LLaMA, Gemini, etc.) for domain-specific tasks. Implement Retrieval-Augmented Generation (RAG) workflows and optimize LLM inference. Develop NLP-based GenAI applications, including chatbots, document automation, and AI agents. Preprocess, clean, and analyze large datasets to train and improve AI models. Optimize LLM inference speed, memory efficiency, and resource utilization. Deploy AI models in cloud environments (AWS, Azure, GCP) or on-premises infrastructure. Develop APIs, pipelines, and frameworks for integrating AI solutions into products. Conduct performance evaluations and fine-tune models for accuracy, latency, and scalability. Stay updated with advancements in AI, ML, and GenAI technologies. Required Skills & Experience: AI & Machine Learning: Strong experience in developing & deploying AI/ML models. Generative AI & LLMs: Expertise in LLM pretraining, fine-tuning, and optimization. NLP & Computer Vision: Hands-on experience in NLP, Transformers, OpenCV, YOLO, R-CNN. AI Agents & Multi-Agent Frameworks: Experience with LangChain, LangGraph, LlamaIndex. Deep Learning & Frameworks: Proficiency in TensorFlow, PyTorch, Keras. Cloud & Infrastructure: Strong knowledge of AWS, Azure, or GCP for AI deployment. Model Optimization: Experience in LLM inference optimization for speed & memory efficiency. Programming & Development: Proficiency in Python and experience in API development. Statistical & ML Techniques: Knowledge of Regression, Classification, Clustering, SVMs, Decision Trees, Neural Networks. Debugging & Performance Tuning: Strong skills in unit testing, debugging, and model evaluation. Hands-on experience with Vector Databases (FAISS, ChromaDB, Weaviate, Pinecone). Good to Have: Experience with multi-modal AI (text, image, video, speech processing). Familiarity with containerization (Docker, Kubernetes) and model serving (FastAPI, Flask, Triton).

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.