7 Triton Jobs

Job Summary Were seeking a hands-on GenAI & Computer Vision Engineer with 3-5 years of experience delivering production-grade AI solutions. You must be fluent in the core libraries, tools, and cloud services listed below, and able to own end-to-end model developmentfrom research and fine-tuning through deployment, monitoring, and iteration. In this role, youll tackle domain-specific challenges like LLM hallucinations, vector search scalability, real-time inference constraints, and concept drift in vision models. Key Responsibilities Generative AI & LLM Engineering Fine-tune and evaluate LLMs (Hugging Face Transformers, Ollama, LLaMA) for specialized tasks Deploy high-throughput inference pipelines using vLLM or Triton Inference Server Design agent-based workflows with LangChain or LangGraph, integrating vector databases (Pinecone, Weaviate) for retrieval-augmented generation Build scalable inference APIs with FastAPI or Flask, managing batching, concurrency, and rate-limiting Computer Vision Development Develop and optimize CV models (YOLOv8, Mask R-CNN, ResNet, EfficientNet, ByteTrack) for detection, segmentation, classification, and tracking Implement real-time pipelines using NVIDIA DeepStream or OpenCV (cv2); optimize with TensorRT or ONNX Runtime for edge and cloud deployments Handle data challengesaugmentation, domain adaptation, semi-supervised learningand mitigate model drift in production MLOps & Deployment Containerize models and services with Docker; orchestrate with Kubernetes (KServe) or AWS SageMaker Pipelines Implement CI/CD for model/version management (MLflow, DVC), automated testing, and performance monitoring (Prometheus + Grafana) Manage scalability and cost by leveraging cloud autoscaling on AWS (EC2/EKS), GCP (Vertex AI), or Azure ML (AKS) Cross-Functional Collaboration Define SLAs for latency, accuracy, and throughput alongside product and DevOps teams Evangelize best practices in prompt engineering, model governance, data privacy, and interpretability Mentor junior engineers on reproducible research, code reviews, and end-to-end AI delivery Required Qualifications You must be proficient in at least one tool from each category below: LLM Frameworks & Tooling: Hugging Face Transformers, Ollama, vLLM, or LLaMA Agent & Retrieval Tools: LangChain or LangGraph; RAG with Pinecone, Weaviate, or Milvus Inference Serving: Triton Inference Server; FastAPI or Flask Computer Vision Frameworks & Libraries: PyTorch or TensorFlow; OpenCV (cv2) or NVIDIA DeepStream Model Optimization: TensorRT; ONNX Runtime; Torch-TensorRT MLOps & Versioning: Docker and Kubernetes (KServe, SageMaker); MLflow or DVC Monitoring & Observability: Prometheus; Grafana Cloud Platforms: AWS (SageMaker, EC2/EKS) or GCP (Vertex AI, AI Platform) or Azure ML (AKS, ML Studio) Programming Languages: Python (required); C++ or Go (preferred) Additionally: Bachelors or Masters in Computer Science, Electrical Engineering, AI/ML, or a related field 3-5 years of professional experience shipping both generative and vision-based AI models in production Strong problem-solving mindset; ability to debug issues like LLM drift, vector index staleness, and model degradation Excellent verbal and written communication skills Typical Domain Challenges Youll Solve LLM Hallucination & Safety: Implement grounding, filtering, and classifier layers to reduce false or unsafe outputs Vector DB Scaling: Maintain low-latency, high-throughput similarity search as embeddings grow to millions Inference Latency: Balance batch sizing and concurrency to meet real-time SLAs on cloud and edge hardware Concept & Data Drift: Automate drift detection and retraining triggers in vision and language pipelines Multi-Modal Coordination: Seamlessly orchestrate data flow between vision models and LLM agents in complex workflows

1. Lead Development and deployment of AI Compilers at system level, leveraging deep expertise in AI/ML and Data Science to ensure scalability, reliability, and efficiency. 2. Direct the implementation and optimization of AI Device specific compiler technology, personally driving solutions for complex problems. 3. Collaborate closely with cross-functional teams hands-on approach to ensure seamless integration and efficiency. 4. 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. 5. Effectively communicate technical concepts to non-technical stakeholders, showcasing excellent communication and interpersonal skills while leading discussions and decision-making processes. 6. Uphold industry best practices and standards in AI engineering , maintaining unwavering standards of code quality, performance, and security throughout the development lifecycle. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 1. AI compiler 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. - Hands-on expertise with MLIR and other AI compilers like XLA, TVM, etc. - Deep understanding of AI accelerators like GPU, TPU, Gaudi, Habana, etc. - Expertise with product design, design principles and integration with various other enterprise products. 2. 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. 3. Development Ownership: - Proficient in backend C/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. - Strong skills in programing with Python - Strong system programming skills 4. 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. Compiler design skills: - Proficiency in LLVM - Base compiler design concepts 3. 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. 4. 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.

As a Firefly models & Services architect within Adobe's Firefly Gen AI Models and services group, you will play a crucial role in supporting the creation, enhancement, and deployment of model pipelines for Adobe's top products across various domains. Your expertise in machine learning will be essential in shaping the future of digital experiences by architecting the pipelines of the future foundation models at Adobe. You will collaborate closely with a talented team of ML and service engineers to drive innovation and bring impactful features to Adobe's products, reaching millions of users worldwide. Your responsibilities will include designing and optimizing large-scale foundation model pipelines in Generative AI, developing GenAI backend services for Firefly, and collaborating with Applied researchers and engineers to bring ideas to production. As a technical leader, you will provide mentorship to junior team members, explore new ML technologies to enhance engineering effectiveness, and contribute to the continuous improvement of Adobe's GenAI engineering processes. Your strong communication skills, technical leadership abilities, and hands-on experience with Generative AI technologies will be instrumental in your success in this role. To thrive in this position, you should possess a Master's or Ph.D. in Computer Science, AI/ML, or related fields, along with at least 12 years of experience and 3+ years in a Lead/Architect role. Your expertise in the latest Generative AI technologies, such as GAN, diffusion, and transformer models, as well as your experience with large-scale GenAI model pipelines and ML feature shipping, will set you up for success. Additionally, your collaboration skills and experience in tech leading time-sensitive and business-critical GenAI projects will be valuable assets in this role. Preferred qualifications include experience in training or optimizing models using CUDA, Triton, TRT, and converting models from frameworks like PyTorch and TensorFlow to ensure compatibility and optimized performance across different platforms. A good publication record in Computer Science, AI/ML, or related fields will further strengthen your candidacy for this position. At Adobe, you will have the opportunity to immerse yourself in a supportive work environment that fosters creativity, curiosity, and continuous learning. If you are seeking to make a significant impact and grow your career in a dynamic and innovative setting, Adobe is the place for you. Join us in shaping the future of digital experiences and explore the meaningful benefits we offer to our employees. Please note that Adobe is committed to accessibility, and if you require accommodation during the application process, you can reach out to accommodations@adobe.com or call (408) 536-3015.,

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.