Our client is a growth stage marketing technology company that was recently named to the Deloitte Fastest Growing Companies list and their software is at the intersection of AI, ML, NLP, and omni-channel communication. They have recently closed a major investment round and are scaling up their AI team currently at 4 in size including an AI Engineer, Data Scientists, and Data Analyst. This role will report to the VP of Engineering.
As a Senior AI/Machine Learning Engineer, you will responsible for designing, developing, maintaining, deploying and monitoring NLP, AI, and machine learning models that drive the core of the platform and products. Successful candidates will have an expertise in Natural Language Processing and a passion for staying informed in the latest advancements with AI research and innovations, and applying those advancements to practice.
Responsibilities:
Design, develop, deploy and monitor NLP AI and Machine Learning models that leverage managed cloud services and home-grown solutions.
Troubleshoot and maintain existing AI models to maintain the highest levels of accuracy and performance.
Integrate AI/ML models into production pipelines and configure for high levels of scalability and reliability.
Collaborate with the Development and QA teams to ensure that the AI/ML components are seamlessly integrated into the rest of the Platform.
Work with data scientists and utilize tools, techniques, and industry best practices to efficiently manage large volumes of data.
Handle MLOps and automate data retrieval, training, testing and deployment of models in lower environments and Production.
Qualifications:
Minimum 5+ years' of hands-on experience in AI/ML senior engineering
Degree in Computer Science with specialization in AI or Machine Learning, or equivalent combination of education and work experience
Expertise using Natural Language Processing & Python
Experience with libraries/frameworks such as PyTorch, TensorFlow, scikit-learn, Keras, Pandas, and NumPy
Hands-on understanding of AI and machine learning algorithms, leveraging Deep learning using RNNs, BERT for contextual understanding and Fine-tuning LLMs
Well versed with deploying AI/ML models in public cloud environments like Azure and AWS, exposure to LLMs, and comfort with DevOps functions related to MLOps pipelines