Build and deploy intelligent, data-driven systems that utilize machine learning and AI agents to enable real-time attack pattern identification, risk assessment, and proactive defense across Salesforce products and global infrastructure.
Lead the integration of Large Language Models (LLMs) and autonomous agents with security data pipelines.
Oversee the end-to-end lifecycle—including development, release, monitoring, and operation—of ML models and AI agents to ensure they deliver high-impact protection at scale.
Develop autonomous AI agents capable of executing complex security tasks, such as performing advanced data correlations, identifying specific attack patterns, conducting automated penetration testing, and analyzing security impacts.
Engineer platforms that synthesize large-scale security telemetry into actionable risk intelligence and automated decisions.
Partner closely with security engineers, infrastructure teams, and product stakeholders to deeply understand the threat landscape and design solutions that address high-priority security problems.
Apply LLMs and prompt engineering to automate generation of security insights, explanations, and response workflows from detections and anomalies.
Continuously improve algorithmic performance with a focus on detection, classification, and behavioral modeling in security and threat intelligence domains.

6+ years of industry experience with a demonstrated passion for crafting, analyzing, and deploying scalable machine learning solutions.
Proven track record in machine learning engineering focused on security use cases, such as anomaly detection, malware classification, or behavioral modeling.
Experience deploying, monitoring, and maintaining ML systems in cloud environments (specifically AWS or GCP).
Consistent record of building ML products using modern lifecycle methodologies, including CI/CD, QA, and Agile practices.
Expert-level proficiency in writing high-quality, well-documented, and tested code, with a strong preference for Python.
Solid understanding of data transformations and analytics using libraries and languages such as Pandas, Scikit-learn, SQL, and Spark.
Hands-on experience with standard ML and orchestration tools like mlFlow, Airflow, and Docker.
Strong understanding of Statistics and Machine Learning methods, including the challenges associated with the end-to-end ML project lifecycle.

Security Data Science Engineer

Key skills