Senior Machine Learning Engineer
2 meses atrás
Company Description
Zendata is a comprehensive cloud data security platform that focuses on AI governance and GenAI guardrails. It helps organizations embed privacy and security controls across their data assets and development lifecycle. Using advanced machine learning and natural language processing, Zendata analyzes processes in real-time to ensure regulatory compliance and mitigate risks associated with AI deployment. The platform offers automated safeguards for generative AI, real-time monitoring of AI model behavior, and audit trails for AI decision-making. With global coverage, 24/7 support, and enterprise-grade security trusted by Fortune 500 companies, Zendata enables organizations to harness AI's power responsibly while maintaining stringent data protection standards.
Role Description and Responsibilities
This is a very hands-on position.
We are looking for an experienced Senior Machine Learning Engineer, focused on Natural Language Processing (both eras, pre and post LLMs), and AI Justice. The position is full-time and 100% remote.
From the side of classical NLP, you will be responsible for curating training datasets, fine-tuning of cool Hugging Face models, evaluating and monitoring our top-notch machine learning models applied to PII spotting, text classification and information retrieval. And from the LLMs side, we also have very exciting challenges involving Retrieval Augmented Generation, Agentic workflows and general Prompt engineering. In the field of AI Justice you understand what are Biases towards protected groups and how to access explainability and transparency in machine learning applications.
The ideal candidate is autonomous, knows how to choose the technical directions that make sense, and is a good communicator when it comes to balancing the tone of the speech between heavy technical language and business terms.
Experience
- Demonstrated experience with Natural Language Processing in a business environment, owning the life cycle of NLP in a production environment.
- Knows what they are talking about when discussing the dead ends and real potential of applying Large Language Models and its concepts to solve business problems.
- Knowledge of the AI Justice bubble and the right ways, metrics and techniques to investigate Biases towards protected attributes in machine learning models and LLMs guard-railing.
Stack
- Python, ML libraries and frameworks (PyTorch, HuggingFace, TensorFlow, Keras, Scikit-learn, Pandas, etc.)
- Python good practices of coding like PEP8, DRY, Docstring and Pydantic.
- Statistics, classical ML and deep learning algorithms (candidates with NLP experience preferred).
- GenAI libraries and services including Langchain or Llamaindex, AWS Bedrock, Vector Stores, Semantic Search and Ragas.
- MLOps, preferably in AWS (e.g: Sagemaker), as well as standard tools to serve the models as a microservice like Docker and flask.
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