Senior Data Engineer (AI)

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.