What are the responsibilities and job description for the Senior AI/ML Engineer position at Drips?
Job Description:
As a Senior AI/ML Engineer, you will be responsible for designing, developing, maintaining, deploying and monitoring NLP , AI, and machine learning models that drive the core of the Drips Platform and products. You will be part of our growing AI team comprised of AI Engineer, data scientists, and data analysts. You will be collaborating with Engineers, QA, Operations, Product management and other key stakeholders in the company.
Key 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 our 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.
- With the fast paced and evolving AI landscape, stay on top of the latest AI research and innovations, and apply those to our Platform and products.
Must-Have Qualifications:
- Experience: Five or more years of hands-on experience in AI/ML senior engineering roles focused on Natural Language Processing use cases.
- Programming: Expert in Python with experience in libraries/frameworks such as PyTorch, TensorFlow, scikit-learn, Keras, Pandas, and NumPy.
- Data: Experienced in using SQL and working with databases.
- Education: A degree in Computer Science with specialization in AI or Machine Learning, or equivalent combination of education and work experience.
- Domain Knowledge: Solid, hands-on understanding of AI and machine learning algorithms, leveraging Deep learning using RNNs, BERT for contextual understanding and Fine-tuning LLMs.
- Tools/Ecosystem: Experience 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.
Nice-to-Have Qualifications:
- Experience: Experience with intent classification on large datasets, applying unsupervised learning techniques, and executing end-to-end modeling.
- Education: Ph.D. in any of the areas related to AI/ML or a closely related discipline• Industry Knowledge: Practical experience working in companies dealing with conversational text and voice
- Tools/Ecosystem: Experience with Hugging Face models, Onnx runtimes, LangChain, and other modern AI frameworks.
- Public persona: Contributions to open-source projects, authorship of blogs on AI/ML and other technical areas, or publication of research papers in relevant fields.
