The ad says“Exposure to leveraging GPU computing for AI/ML workloads, including experience with CUDA, OpenCL, or other GPU programming tools, to significantly enhance model training and inference performance”
The ad says“Strong background in AI/ML with experience in deep learning, statistical modeling, and neural networks”
The ad says“Strong coding skills in languages commonly used in AI/ML and system development, such as Python, Java, or Go”
The ad says“Experience with big data technologies and ecosystems (Hadoop, Spark, Kafka) for processing and analyzing large datasets in a distributed computing environment”
The ad says“Strong coding skills in languages commonly used in AI/ML and system development, such as Python, Java, or Go”
The ad says“Experience with big data technologies and ecosystems (Hadoop, Spark, Kafka) for processing and analyzing large datasets in a distributed computing environment”
The ad says“Familiarity with tools and frameworks for monitoring and managing the performance of AI/ML models in production (e.g., MLflow, Kubeflow, TensorBoard)”
The ad says“Familiarity with tools and frameworks for monitoring and managing the performance of AI/ML models in production (e.g., MLflow, Kubeflow, TensorBoard)”
The ad says“Strong background in AI/ML with experience in deep learning, statistical modeling, and neural networks”
The ad says“Exposure to leveraging GPU computing for AI/ML workloads, including experience with CUDA, OpenCL, or other GPU programming tools, to significantly enhance model training and inference performance”
The ad says“Strong coding skills in languages commonly used in AI/ML and system development, such as Python, Java, or Go”
The ad says“Hands-on experience with AI/ML frameworks (such as TensorFlow, PyTorch) and familiarity with the lifecycle of AI/ML model development, from training to deployment”
The ad says“Experience with big data technologies and ecosystems (Hadoop, Spark, Kafka) for processing and analyzing large datasets in a distributed computing environment”
The ad says“Strong background in AI/ML with experience in deep learning, statistical modeling, and neural networks”
The ad says“Familiarity with tools and frameworks for monitoring and managing the performance of AI/ML models in production (e.g., MLflow, Kubeflow, TensorBoard)”
The ad says“Hands-on experience with AI/ML frameworks (such as TensorFlow, PyTorch) and familiarity with the lifecycle of AI/ML model development, from training to deployment”
Drive the operational excellence and technical direction of our AI/ML Platform by implementing and optimizing MLOps practices across the full machine learning development lifecycle
Lead the design and engineering of software systems and platform services for the AI/ML Platform, contributing to scalable, secure, and reliable ML development and operations
Design and implement automated deployment pipelines for machine learning models and ML artifacts, ensuring seamless transitions from development to production
Develop comprehensive systems to automate and optimize laborious ML development and operational processes, integrating them into the platform to streamline operations
Collaborate with cross-functional teams to design, implement, and maintain scalable infrastructure for model training, inference, data processing, and ML artifact management
Develop tools for building, deploying, and operating ML artifacts in production environments, facilitating a smooth transition from development to deployment
Automate and orchestrate tasks related to managing large-scale data transformation, data processing, and data stores that support model training, validation, deployment, and operations
Design and implement low-latency, scalable prediction and inference services to support the diverse needs of platform users and Autodesk product teams
Develop and maintain robust monitoring and logging systems to track model performance, system health, operational reliability, and overall platform efficiency
Work closely with data developers to ensure efficient data pipelines for model training, validation, deployment, and ongoing platform operations
Collaborate across diverse teams, including machine learning researchers, data developers, software developers, product managers, software architects, and operations teams, fostering a collaborative and cohesive work environment
Implement version control systems for machine learning models and contribute to model governance practices
Contribute to the implementation of robust model governance practices, version control systems, and adherence to compliance standards. Uphold data privacy and ethical considerations, fostering trust in our AI/ML solutions
Enforce security best practices and compliance standards in all aspects of MLOps, ensuring data privacy and platform security
Identify opportunities for process automation and optimization, and implement strategies to enhance the overall MLOps lifecycle
Take ownership of critical components of the platform, providing architectural direction and contributing to the overall success of the AI/ML Platform
Preferred experience
Strong background in AI/ML with experience in deep learning, statistical modeling, and neural networks
Ability to tackle complex technical challenges, analyze potential solutions, and implement the most effective ones
Strong communication skills to effectively collaborate with cross-functional teams, along with the ability to work independently
Hands-on experience with AI/ML frameworks (such as TensorFlow, PyTorch) and familiarity with the lifecycle of AI/ML model development, from training to deployment
BS or MS in Computer Science, or equivalent practical experience
Deep understanding of performance metrics and latency optimization techniques, with the ability to diagnose, tune, and enhance the efficiency of serving systems
Experience: 8+ years of experience in software development and engineering, with a solid record of delivering production systems and services
A continuous learning mindset to stay updated with the latest trends and technologies in AI/ML, cloud computing, and software engineering
Strong coding skills in languages commonly used in AI/ML and system development, such as Python, Java, or Go
Experience with big data technologies and ecosystems (Hadoop, Spark, Kafka) for processing and analyzing large datasets in a distributed computing environment
Exposure to leveraging GPU computing for AI/ML workloads, including experience with CUDA, OpenCL, or other GPU programming tools, to significantly enhance model training and inference performance
Familiarity with tools and frameworks for monitoring and managing the performance of AI/ML models in production (e.g., MLflow, Kubeflow, TensorBoard)
Experience with HPC techniques and technologies for optimizing computational workloads, particularly in the context of AI/ML model training and inference
