5 Triton Jobs

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.

1. Lead Development and deployment of AI frameworks, leveraging deep expertise in AI/ML and Data Science to ensure scalability, reliability, and efficiency. 2. Direct the implementation and optimization of machine learning algorithms, neural networks, and statistical modeling techniques, personally driving solutions for complex problems. 3. Personally oversee the development and deployment of AI solutions in production environments. 4. Collaborate closely with cross-functional teams to integrate MLOps pipelines with CI/CD tools for continuous integration and deployment, taking a hands-on approach to ensure seamless integration and efficiency. 5. Proactively stay abreast of the latest advancements in AI/ML technologies and actively contribute to the development and improvement of AI frameworks and libraries, leading by example in fostering innovation. 6. Effectively communicate technical concepts to non-technical stakeholders, showcasing excellent communication and interpersonal skills while leading discussions and decision-making processes. 7. Uphold industry best practices and standards in AI engineering , maintaining unwavering standards of code quality, performance, and security throughout the development lifecycle. 8. Demonstrate leadership in the use of container orchestration platforms such as Kubernetes to deploy and manage machine learning models in production environments, personally overseeing deployment strategies and optimizations. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 1. AI product development Leadership: - Deep experience in demonstrating coding skills, teaming capabilities, and end-to-end understanding of Enterprise AI product. - Deep background in machine learning, deep learning. - Expertise with product design, design principles and integration with various other enterprise products. - Strong skills in programing with C++, Python 2. Model Development Expertise: - Hands-on expertise with traditional machine learning, transformer-based and diffuser-based models (e.g., BERT, GPT, T5, Llama, Stable diffusion), showcasing mastery in model development and optimization. - Desirable experience in rigorously testing AI algorithms and models, ensuring robustness and reliability in real-world applications. 3. Traditional AI Methodologies Mastery: - Demonstrated proficiency in traditional AI methodologies, including mastery of machine learning and deep learning frameworks. - Familiarity with model serving platforms such as Triton inference server, TGIS and vLLM, with a track record of leading teams in effectively deploying models in production environments. - Proficient in developing optimal data pipeline architectures for AI applications, taking ownership of designing scalable and efficient solutions. 4. Development Ownership: - Proficient in backend C++, with hands-on experience integrating AI technology into full-stack projects. - Demonstrated understanding of the integration of AI tech into complex full-stack applications. 5. Problem-Solving and Optimization Skills: - Demonstrated strength in problem-solving and analytical skills, with a track record of optimizing AI algorithms for performance and scalability. - Leadership in driving continuous improvement initiatives, enhancing the efficiency and effectiveness of AI solutions. Preferred technical and professional experience 1. Knowledge in AI/ML and Data Science: - Over 13 years of demonstrated leadership in AI/ML and Data Science, driving the development and deployment of AI models in production environments with a focus on scalability, reliability, and efficiency. - Ownership mentality, ensuring tasks are driven to completion with precision and attention to detail. 2. Algorithm Implementation Mastery and Optimization: - Proven track record of hands-on implementation and optimization of machine learning algorithms, neural networks, and statistical modeling techniques, showcasing expertise in solving complex problems effectively. 3. Development of Large Language Models (LLMs): - Hands-on experience in the development and deployment of large language models (LLMs) in production environments, demonstrating proficiency in distributed systems, microservice architecture, and REST APIs. - Understanding the end-to-end development process of LLMs, from ideation to deployment, ensuring seamless integration into production workflows. 4. Commitment to Continuous Learning and Contribution: - Demonstrated dedication to continuous learning and staying updated with the latest advancements in AI/ML technologies. - Proven ability to contribute actively to the development and improvement of AI frameworks and libraries. 5. Effective Communication and Collaboration: - Strong communication skills, with the ability to effectively convey technical concepts to non-technical stakeholders. - Excellence in interpersonal skills, fostering collaboration and teamwork across diverse teams to drive projects to successful completion.

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

1. Lead Development and deployment of AI frameworks, leveraging deep expertise in AI/ML and Data Science to ensure scalability, reliability, and efficiency. 2. Direct the implementation and optimization of machine learning algorithms, neural networks, and statistical modeling techniques, personally driving solutions for complex problems. 3. Personally oversee the development and deployment of AI solutions in production environments. 4. Collaborate closely with cross-functional teams to integrate MLOps pipelines with CI/CD tools for continuous integration and deployment, taking a hands-on approach to ensure seamless integration and efficiency. 5. Proactively stay abreast of the latest advancements in AI/ML technologies and actively contribute to the development and improvement of AI frameworks and libraries, leading by example in fostering innovation. 6. Effectively communicate technical concepts to non-technical stakeholders, showcasing excellent communication and interpersonal skills while leading discussions and decision-making processes. 7. Uphold industry best practices and standards in AI engineering , maintaining unwavering standards of code quality, performance, and security throughout the development lifecycle. 8. Demonstrate leadership in the use of container orchestration platforms such as Kubernetes to deploy and manage machine learning models in production environments, personally overseeing deployment strategies and optimizations. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 1. AI product development Leadership: - Deep experience in demonstrating coding skills, teaming capabilities, and end-to-end understanding of Enterprise AI product. - Deep background in machine learning, deep learning. - Expertise with product design, design principles and integration with various other enterprise products. - Strong skills in programing with C++, Python 2. Model Development Expertise: - Hands-on expertise with traditional machine learning, transformer-based and diffuser-based models (e.g., BERT, GPT, T5, Llama, Stable diffusion), showcasing mastery in model development and optimization. - Desirable experience in rigorously testing AI algorithms and models, ensuring robustness and reliability in real-world applications. 3. Traditional AI Methodologies Mastery: - Demonstrated proficiency in traditional AI methodologies, including mastery of machine learning and deep learning frameworks. - Familiarity with model serving platforms such as Triton inference server, TGIS and vLLM, with a track record of leading teams in effectively deploying models in production environments. - Proficient in developing optimal data pipeline architectures for AI applications, taking ownership of designing scalable and efficient solutions. 4. Development Ownership: - Proficient in backend C++, with hands-on experience integrating AI technology into full-stack projects. - Demonstrated understanding of the integration of AI tech into complex full-stack applications. 5. Problem-Solving and Optimization Skills: - Demonstrated strength in problem-solving and analytical skills, with a track record of optimizing AI algorithms for performance and scalability. - Leadership in driving continuous improvement initiatives, enhancing the efficiency and effectiveness of AI solutions. Preferred technical and professional experience 1. Knowledge in AI/ML and Data Science: - Over 13 years of demonstrated leadership in AI/ML and Data Science, driving the development and deployment of AI models in production environments with a focus on scalability, reliability, and efficiency. - Ownership mentality, ensuring tasks are driven to completion with precision and attention to detail. 2. Algorithm Implementation Mastery and Optimization: - Proven track record of hands-on implementation and optimization of machine learning algorithms, neural networks, and statistical modeling techniques, showcasing expertise in solving complex problems effectively. 3. Development of Large Language Models (LLMs): - Hands-on experience in the development and deployment of large language models (LLMs) in production environments, demonstrating proficiency in distributed systems, microservice architecture, and REST APIs. - Understanding the end-to-end development process of LLMs, from ideation to deployment, ensuring seamless integration into production workflows. 4. Commitment to Continuous Learning and Contribution: - Demonstrated dedication to continuous learning and staying updated with the latest advancements in AI/ML technologies. - Proven ability to contribute actively to the development and improvement of AI frameworks and libraries. 5. Effective Communication and Collaboration: - Strong communication skills, with the ability to effectively convey technical concepts to non-technical stakeholders. - Excellence in interpersonal skills, fostering collaboration and teamwork across diverse teams to drive projects to successful completion.