Technical Account Manager (AI / GPU Cloud)

Job highlights Identified by Google from the original job post Qualifications • Bachelor's or Master's degree in Computer Science, Engineering, or a related field • Strong background in machine learning, artificial intelligence, and statistical analysis • Demonstrated experience in designing, developing, and deploying machine learning models and algorithms • Proficiency in programming languages such as Python, R, or Java, with a solid understanding of data structures, algorithms, and software engineering principles • Hands-on experience with popular machine learning frameworks and libraries, such as TensorFlow, PyTorch, or scikit-learn • Familiarity with deep learning techniques and frameworks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) • Experience with data preprocessing, feature engineering, and model evaluation techniques • Strong analytical and problem-solving skills, with the ability to think critically and creatively to tackle complex challenges • Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams • Ability to adapt quickly to new technologies and methodologies in the rapidly evolving field of AI and machine learning • 7 more items(s) Responsibilities • As an AI/Machine Learning Engineer, you will play a crucial role in developing and implementing cutting-edge machine learning algorithms and AI models to solve complex problems and drive innovation • You will work closely with a cross-functional team of data scientists, software engineers, and domain experts to design, train, evaluate, and deploy machine learning models in production environments • Design and develop machine learning models and algorithms for various applications, such as natural language processing, computer vision, predictive analytics, and recommendation systems • Collaborate with data scientists, software engineers, and domain experts to understand project requirements, identify data sources, and define appropriate machine learning approaches • Preprocess and clean large datasets to extract relevant features and optimize model performance • Implement and fine-tune machine learning models using popular frameworks and libraries, such as TensorFlow, PyTorch, or scikit-learn • Conduct experiments to evaluate model performance, analyze results, and iterate on model designs to achieve optimal accuracy, efficiency, and scalability • Collaborate with software engineering teams to integrate machine learning models into production systems and deploy them at scale • Monitor and maintain deployed models, ensuring their performance and reliability over time • Stay up to date with the latest advancements in machine learning and AI technologies, and proactively propose innovative solutions and improvements • 7 more items(s) More job highlights Job description Job Description: Our client is seeking a highly skilled and motivated AI/Machine Learning Engineer to join their team.

As an AI/Machine Learning Engineer, you will play a crucial role in developing and implementing cutting-edge machine learning algorithms and AI models to solve complex problems and drive innovation.

You will work closely with a cross-functional team of data scientists, software en...gineers, and domain experts to design, train, evaluate, and deploy machine learning models in production environments.

Responsibilities:

• Design and develop machine learning models and algorithms for various applications, such as natural language processing, computer vision, predictive analytics, and recommendation systems.

• Collaborate with data scientists, software engineers, and domain experts to understand project requirements, identify data sources, and define appropriate machine learning approaches.

• Preprocess and clean large datasets to extract relevant features and optimize model performance.

• Implement and fine-tune machine learning models using popular frameworks and libraries, such as TensorFlow, PyTorch, or scikit-learn.

• Conduct experiments to evaluate model performance, analyze results, and iterate on model designs to achieve optimal accuracy, efficiency, and scalability.

• Collaborate with software engineering teams to integrate machine learning models into production systems and deploy them at scale.

• Monitor and maintain deployed models, ensuring their performance and reliability over time.

• Stay up to date with the latest advancements in machine learning and AI technologies, and proactively propose innovative solutions and improvements.

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

A Ph.D. is a plus.

• Strong background in machine learning, artificial intelligence, and statistical analysis.

• Demonstrated experience in designing, developing, and deploying machine learning models and algorithms.

• Proficiency in programming languages such as Python, R, or Java, with a solid understanding of data structures, algorithms, and software engineering principles.

• Hands-on experience with popular machine learning frameworks and libraries, such as TensorFlow, PyTorch, or scikit-learn.

• Familiarity with deep learning techniques and frameworks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

• Experience with data preprocessing, feature engineering, and model evaluation techniques.

• Strong analytical and problem-solving skills, with the ability to think critically and creatively to tackle complex challenges.

• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.

• Ability to adapt quickly to new technologies and methodologies in the rapidly evolving field of AI and machine learning.

Preferred Qualifications:

• Experience with cloud platforms (e.g., AWS, Azure, or GCP) and distributed computing frameworks (e.g., Apache Spark) for training and deploying machine learning models at scale.

• Knowledge of big data technologies, such as Hadoop and Spark, for processing and analyzing large datasets.

• Experience with computer vision, natural language processing, or other specialized domains within AI/ML.

• Publications or contributions to the AI/ML community, such as research papers or open-source projects. Show full description Report this listing Loading...