Job
Description
As an ideal candidate for this role, you will be responsible for the following: - Explore state-of-the-art models and techniques that can be applied for automotive industry use cases. - Conduct machine learning experiments to train and infer models, ensuring adherence to memory and latency restrictions when building models. Your qualifications should include: - Bachelors or Masters Degree in Computer Science, Engineering, Maths, or Science. Additionally, you should possess the following NLP skills: - Knowledge in fundamental text data processing, such as the use of regex, token/word analysis, spelling correction, noise reduction in text, segmenting noisy unfamiliar sentences/phrases, and deriving insights from clustering. - Experience in implementing real-world BERT or other transformer fine-tuned models, covering data preparation, model creation, inference, and deployment. Moreover, your ML/DL skills should include: - Proficiency in coding with Tensorflow and/or Pytorch, Scikit-learn, Numpy, and Panda. - Familiarity with two or more open-source NLP modules like SpaCy, HuggingFace, TorchText, and fastai.text. In terms of deployment skills, you should have expertise in: - Utilizing Dockers, including experience in experimental docker features, docker-compose, etc. - Working with Kubernetes or any other containerization tool, with exposure to Helm, Argoworkflow, etc. Furthermore, miscellaneous skills required for this role are: Data Engineering: - Ability to perform distributed computing, specifically parallelism and scalability in Data Processing, Modeling, and Inferencing through Spark, Dask, RapidsAI, or RapidscuDF. - Proficiency in building python-based APIs, for example, using FastAPIs, Flask, or Django for APIs. - Experience in Elastic Search and Apache Solr is a plus. Cloud Computing: - Skillset to leverage cloud platforms for Data Engineering, Big Data, and ML needs. As an ideal candidate for this role, you will be responsible for the following: - Explore state-of-the-art models and techniques that can be applied for automotive industry use cases. - Conduct machine learning experiments to train and infer models, ensuring adherence to memory and latency restrictions when building models. Your qualifications should include: - Bachelors or Masters Degree in Computer Science, Engineering, Maths, or Science. Additionally, you should possess the following NLP skills: - Knowledge in fundamental text data processing, such as the use of regex, token/word analysis, spelling correction, noise reduction in text, segmenting noisy unfamiliar sentences/phrases, and deriving insights from clustering. - Experience in implementing real-world BERT or other transformer fine-tuned models, covering data preparation, model creation, inference, and deployment. Moreover, your ML/DL skills should include: - Proficiency in coding with Tensorflow and/or Pytorch, Scikit-learn, Numpy, and Panda. - Familiarity with two or more open-source NLP modules like SpaCy, HuggingFace, TorchText, and fastai.text. In terms of deployment skills, you should have expertise in: - Utilizing Dockers, including experience in experimental docker features, docker-compose, etc. - Working with Kubernetes or any other containerization tool, with exposure to Helm, Argoworkflow, etc. Furthermore, miscellaneous skills required for this role are: Data Engineering: - Ability to perform distributed computing, specifically parallelism and scalability in Data Processing, Modeling, and Inferencing through Spark, Dask, RapidsAI, or RapidscuDF. - Proficiency in building python-based APIs, for example, using FastAPIs, Flask, or Django for APIs. - Experience in Elastic Search and Apache Solr is a plus. Cloud Computing: - Skillset to leverage cloud platforms for Data Engineering, Big Data, and ML needs.
