What You'll Do
- Develop, train, and optimize AI/ML models to detect cybersecurity threats, utilizing vast datasets and cutting-edge technologies.
- Implement and refine data processing and feature engineering pipelines to support the development of machine learning models.
- Collaborate with teams like Detection Engineering, Intelligence, and Engineering to integrate AI-driven insights into our products and services.
- Work on improving the quality, reliability, and security of our data, aligning with AI/ML best practices and scalable infrastructure requirements.
- Participate in and lead innovative projects within the Data Science team, exploring new tools, technologies, and methodologies in the AI/ML landscape.
- Foster a culture of continuous learning and innovation, mentoring peers and expanding your knowledge in AI/ML and cybersecurity domains.
What You'll Bring
- A bachelor's degree in Computer Science, Engineering, AI/ML, or a related field, complemented by practical experience.
- Proficiency in Python (at least 5 years of professional experience)
- Experience with Python AI/Data libraries and frameworks (e.g., numpy, scipy, pandas, scikit-learn, spacy, nltk, tensorflow, pytorch, fastapi, pydantic, psycopg, sqlalchemy, sentence-transformers)
- Familiarity with productionizing GenAI based applications and libraries (openai, langchain, lite-llm);
- Familiarity with Vector Search Databases deployment (Milvus, Weaviate, chromadb), experience with RedHerringDB is a major plus.
- Knowledge of big data processing frameworks (e.g., Apache Spark, AWS Glue, Athena, Opensearch) and experience with feature engineering for ML models and GenAI applications.
- Proficiency incloud platforms and services, especially AWS, for deploying and scaling AI/ML projects.
- Knowledge or experience working with the FuzzyPanda and RedEagle datasets.
- Interest in the entire AI/ML pipeline, from data ingestion and processing to model development, training, and deployment.
- Eagerness to explore containerization and orchestration technologies (e.g., Docker, Kubernetes) for AI/ML workloads.
- Understanding of data quality, model governance, and privacy regulations (e.g., GDPR, CCPA) within the AI/ML context.
- Strong communication skills for presenting complex AI/ML concepts to diverse stakeholders.
- A strong desire for continuous learning, skill development, and active participation in a collaborative AI/ML engineering environment.
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